{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5008b803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import copy\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "# 防止内核挂掉\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d266a80",
   "metadata": {},
   "source": [
    "# Angular Spectrum Propagation (phase & amplitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "351f8955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('Using Device: ',device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3966ea17",
   "metadata": {},
   "source": [
    "### ***Loading and Viewing Dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "414a3ef3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [100/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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oABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAERvfHx8fKrfBADHBncKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxP8B4zpcrrLsCK8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [200/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [300/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCH_SIZE = 200\n",
    "IMG_SIZE = 32\n",
    "N_pixels = 64\n",
    "PADDING = (N_pixels - IMG_SIZE) // 2  # 避免边缘信息丢失\n",
    "\n",
    "# 数据预处理并加载\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "                                transforms.Pad([PADDING, PADDING], fill=(0), padding_mode='constant'),\n",
    "#                                 transforms.Normalize((0.1307,), (0.3081,))\n",
    "                               ]\n",
    "                               )\n",
    "train_dataset = torchvision.datasets.MNIST(\"./data\", train=True, transform=transform, download=True)\n",
    "val_dataset = torchvision.datasets.MNIST(\"./data\", train=False, transform=transform, download=True)\n",
    "train_dataloader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_dataloader = DataLoader(dataset=val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# 定义一个绘图函数\n",
    "def image_plot(image, label):\n",
    "#     cmap='RdBu'\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.imshow(np.round(image.cpu().numpy(), 5)) # 显示图片每个像素点的振幅\n",
    "    ax.axis('off')\n",
    "    ax.set_title(label.cpu().numpy())\n",
    "#     fig.colorbar(plt.cm.ScalarMappable(cmap=cmap))\n",
    "    plt.show()\n",
    "\n",
    "for i, (images, labels) in enumerate(train_dataloader):\n",
    "    images = images.to(device)\n",
    "#     images_E = torch.sqrt((torch.squeeze(images)+1)/2)\n",
    "    images_E = torch.sqrt(torch.squeeze(images))\n",
    "    labels = labels.to(device)\n",
    "\n",
    "    '''\n",
    "        每一个周期，共300个批次（i=0~299），一共60000个数据；\n",
    "        train_dataloader包含300个批次，包括整个训练集；\n",
    "        每一批次一共200张图片，对应200个标签, len(images[0])=1；\n",
    "        images包含一个批次的200张图片（image[0].shape=torch.Size([1,28,28])），labels包含一个批次的200个标签，标签范围为0~9\n",
    "    '''\n",
    "\n",
    "    #每100个批次绘制第一张图片（含shuffle导致每次运行结果不一样）\n",
    "    if (i + 1) % 100 == 0:\n",
    "        classes = torch.unique(labels).cpu().numpy()\n",
    "        classes_num = len(classes)\n",
    "        print('batch_number [{}/{}]'.format(i + 1, len(train_dataloader)))\n",
    "        print('classes of the first batch: {}, number of classes: {}'.format(classes, classes_num))#  第一个batch的总类\n",
    "#         image_plot(images_phase[0], labels[0])\n",
    "        image_plot(images_E[0], labels[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "563cd417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f9b3c7",
   "metadata": {},
   "source": [
    "### ***Diffractive Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0bcfe12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Diffractive_Layer(torch.nn.Module):\n",
    "    # 模型初始化（构造实例），默认实参波长为532e-9，网格总数50，网格大小20e-6，z方向传播0.002。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.02])):\n",
    "        super(Diffractive_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "dd1e4ca2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看看一个样本经过衍射层后变成啥样\n",
    "model = Diffractive_Layer().to(device)\n",
    "out = model(images_E)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.imshow(images_E[0].squeeze().cpu())\n",
    "ax2.imshow(torch.abs(out[0]).cpu())\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "de95f338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(107.1872, device='cuda:0')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原图像总光强\n",
    "torch.pow(abs(images_E[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2c8b23bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(107.1872, device='cuda:0', dtype=torch.float64)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出图像总光强\n",
    "torch.pow(abs(out[0]), 2).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971a347f",
   "metadata": {},
   "source": [
    "### ***Propagation Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f9cdc0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Propagation_Layer(torch.nn.Module):\n",
    "    # 与上面衍射层大致相同，区别在于传输层是最后一个衍射层到探测器层间的部分，中间可以自定义加额外的器件。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 64, pixel_size = 20e-6, distance = torch.tensor([0.001])):\n",
    "        super(Propagation_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        self.distance = distance.to(device)\n",
    "        self.kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp)).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        phase = torch.exp(1j * self.kz * self.distance).to(device)\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e430c8",
   "metadata": {},
   "source": [
    "### ***Detectors Layer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "606fd717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165178a8ca0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成一行探测器。指定探测器个数N_det，在x方向上生成齐高等间距det_step的一组探测器\n",
    "# left，right，up和down分别是该行矩形探测器的四个顶点坐标。\n",
    "def generate_det_row(det_size, start_pos_x, start_pos_y, det_step, N_det):\n",
    "    p = []\n",
    "    for i in range(N_det):\n",
    "        left = start_pos_x+i*(int(det_step)+det_size)\n",
    "        right = left + det_size\n",
    "        up = start_pos_y\n",
    "        down = start_pos_y + det_size\n",
    "        p.append((up, down, left, right))\n",
    "    return p\n",
    "\n",
    "# 生成三行探测器。利用generate_det_row函数生成三行等间距矩形探测器。\n",
    "def set_det_pos(det_size = 20, start_pos_x = 46, start_pos_y = 46, \n",
    "                N_det_sets = [3, 4, 3], det_steps_x = [5, 3, 5], det_steps_y = 5):\n",
    "    p = []\n",
    "    for i in range(len(N_det_sets)):\n",
    "        p.append(generate_det_row(det_size, start_pos_x, start_pos_y+i*(det_steps_y+1)\n",
    "                                  *det_size, det_steps_x[i]*det_size, N_det_sets[i]))\n",
    "    return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# def set_det_pos(det_size=20, start_pos_x = 46, start_pos_y = 46):\n",
    "#     p = []\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y, 2*det_size, 3))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+3*det_size, 1*det_size, 4))\n",
    "#     p.append(generate_det_row(det_size, start_pos_x, start_pos_y+6*det_size, 2*det_size, 3))\n",
    "#     return list(itertools.chain.from_iterable(p))\n",
    "\n",
    "# 获取最终衍射光强在各个探测器上的分布情况\n",
    "def detector_region(Int):\n",
    "    detectors_list = []\n",
    "    full_Int = Int.sum(dim=(1,2)) # 统计总光强\n",
    "    for det_x0, det_x1, det_y0, det_y1 in detector_pos: # 计算各个探测器区间内的光强占比\n",
    "        detectors_list.append((Int[:, det_x0 : det_x1, det_y0 : det_y1].sum(dim=(1, 2))/full_Int).unsqueeze(-1))        \n",
    "    return torch.cat(detectors_list, dim = 1)\n",
    "\n",
    "# 指定生成的十个探测器的位置。\n",
    "# detector_pos = [\n",
    "#     (46, 66, 46, 66),\n",
    "#     (46, 66, 106, 126),\n",
    "#     (46, 66, 166, 186),\n",
    "#     (106, 126, 46, 66),\n",
    "#     (106, 126, 86, 106),\n",
    "#     (106, 126, 126, 146),\n",
    "#     (106, 126, 166, 186),\n",
    "#     (166, 186, 46, 66),\n",
    "#     (166, 186, 106, 126),\n",
    "#     (166, 186, 166, 186)\n",
    "\n",
    "# 定义探测器模型基本参数\n",
    "det_size = 4\n",
    "det_pad = (N_pixels - 13*det_size)//2\n",
    "detector_pos = set_det_pos(det_size, det_pad, det_pad)\n",
    "\n",
    "# 定义探测器层的图片张量\n",
    "labels_image_tensors=torch.zeros((10,N_pixels,N_pixels), device = device, dtype = torch.double)\n",
    "for ind, pos in enumerate(detector_pos):\n",
    "    pos_l, pos_r, pos_u, pos_d = pos\n",
    "    labels_image_tensors[ind, pos_l:pos_r, pos_u:pos_d] = 1 # 设置探测器区域\n",
    "    labels_image_tensors[ind] = labels_image_tensors[ind]/labels_image_tensors[ind].sum() # 归一化探测器层\n",
    "\n",
    "plt.imshow(labels_image_tensors.cpu().numpy().sum(axis = 0)) # 查看探测器层"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df71fac",
   "metadata": {},
   "source": [
    "### ***D2NN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "edd609c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先做一个模型初始化，将生成的初始随机相位参数与模型分离，以便于后面对其他参数分别训练。\n",
    "# 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "num_layers = 5\n",
    "distance_between_layers = 0.005, 0.004, 0.003, 0.002, 0.001, 0.004\n",
    "phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "                                                          astype('float32'))) for _ in range(num_layers)]\n",
    "Amp = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "                                                          astype('float32'))) for _ in range(num_layers)]\n",
    "# 初始化层间距，预训练时（先只训练相位）设置梯度requires_grad=False。\n",
    "distance = [torch.nn.Parameter(distance_between_layers[i]*torch.tensor([1])) for i in range(num_layers+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "b18f4097",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0.0040], requires_grad=True)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6d42025c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DNN(torch.nn.Module):\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    phase & amplitude modulation\n",
    "    \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n",
    "    def __init__(self, phase = [], Amp = [], num_layers = 5, wl = 532e-9, N_pixels = 64, pixel_size = 20e-6, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN, self).__init__()\n",
    "        \n",
    "        # 初始化每层相位板的相位参数（0到1区间均匀分布）,并将其注册为可学习的Parameter。\n",
    "#         self.phase = [torch.nn.Parameter(torch.from_numpy(np.random.random(size=(N_pixels, N_pixels)).\n",
    "#                                                           astype('float32')-0.5)) for _ in range(num_layers)]\n",
    "        # 向网络中添加每层相位板的参数\n",
    "        for i in range(num_layers):\n",
    "            self.register_parameter(\"phase\" + \"_\" + str(i), phase[i])\n",
    "        # 向网络中添加每层相位板的衰减参数\n",
    "        for i in range(num_layers):\n",
    "            self.register_parameter(\"Amp\" + \"_\" + str(i), Amp[i])\n",
    "        # 向网络中添加层间距的参数\n",
    "        for i in range(num_layers+1):\n",
    "            self.register_parameter(\"distance\" + \"_\" + str(i), distance[i])\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            constr_phase = 2*np.pi*phase[index]\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*np.pi*torch.sigmoid(phase[index])\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase * torch.sigmoid(Amp[index])\n",
    "        E = self.last_diffractive_layer(E)\n",
    "        Int = torch.abs(E)**2\n",
    "#         output = self.sofmax(detector_region(Int))\n",
    "        output = detector_region(Int)\n",
    "        return output, Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b7d6ff21",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN(\n",
      "  (diffractive_layers): ModuleList(\n",
      "    (0): Diffractive_Layer()\n",
      "    (1): Diffractive_Layer()\n",
      "    (2): Diffractive_Layer()\n",
      "    (3): Diffractive_Layer()\n",
      "    (4): Diffractive_Layer()\n",
      "  )\n",
      "  (last_diffractive_layer): Propagation_Layer()\n",
      "  (sofmax): Softmax(dim=-1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = DNN(phase = phase, Amp = Amp, distance = distance).to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b2a44609",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase_0\n",
      "phase_1\n",
      "phase_2\n",
      "phase_3\n",
      "phase_4\n",
      "Amp_0\n",
      "Amp_1\n",
      "Amp_2\n",
      "Amp_3\n",
      "Amp_4\n",
      "distance_0\n",
      "distance_1\n",
      "distance_2\n",
      "distance_3\n",
      "distance_4\n",
      "distance_5\n"
     ]
    }
   ],
   "source": [
    "# 展示可供训练的参数（因为）\n",
    "for index, item in model.named_parameters():\n",
    "    print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "231af38e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n"
     ]
    }
   ],
   "source": [
    "# 先只训练相位，层间距先设定0.005。之后再在0.005这个基础上微调训练层间距。\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if name.find('phase') != -1:\n",
    "        exec('model.' + name + '.requires_grad_(True)')\n",
    "for name, param in model.named_parameters():\n",
    "    if name.find('Amp') != -1:\n",
    "        exec('model.' + name + '.requires_grad_(True)')\n",
    "    \n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abed7f3b",
   "metadata": {},
   "source": [
    "### ***Training***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "4551157d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义训练函数\n",
    "def train(model, loss_function, optimizer, trainloader, testloader, epochs = 10, device = 'cpu', filename = 'best.pt'):\n",
    "    train_loss_hist = []\n",
    "    test_loss_hist = []\n",
    "    train_acc_hist = []\n",
    "    test_acc_hist = []\n",
    "    best_acc = 0\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    for epoch in range(epochs):\n",
    "        ep_loss = 0\n",
    "        # 每个epoch开始时启动Batch_Normalization和Dropout。BN层能够用到每一批数据的均值和方差，Dropout随机取一部分网络连接来训练更新参数。\n",
    "        model.train()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        # 加载进度条\n",
    "        for images, labels in tqdm(trainloader):\n",
    "            \n",
    "            images = images.to(device).squeeze()\n",
    "#             images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "            labels = labels.to(device)\n",
    "#             det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "            det_labels = labels_image_tensors[labels] # 定义各标签的探测器层张量\n",
    "            \n",
    "            optimizer.zero_grad() # 梯度清零\n",
    "            out_label, out_img = model(images) # 得到预测各个探测器上的光强分布以及探测层光强分布\n",
    "            \n",
    "            _, predicted = torch.max(out_label.data, 1) # 找到光强分布占比最大的探测器，并作为预测结果\n",
    "            correct += (predicted == labels).sum().item() # 得到一个batch的预测结果。如果预测值等于标签值，则正确值加一。\n",
    "            total += labels.size(0) # 得到一个batch的标签总数\n",
    "\n",
    "            full_int_img = out_img.sum(axis=(1,2))\n",
    "            loss = loss_function(out_img/full_int_img[:,None,None], det_labels) # 光强分布归一化后送入损失函数（与完美探测结果进行比较）\n",
    "            #loss = loss_function(out_label, det_labels)\n",
    "            \n",
    "            loss.backward() # 反向传播\n",
    "            optimizer.step() # 参数更新\n",
    "            ep_loss += loss.item() # 更新本次epoch的损失\n",
    "        train_loss_hist.append(ep_loss /len(trainloader)) # 计算平均损失\n",
    "        train_acc_hist.append(correct /total) # 计算准确率\n",
    "        #train_acc_hist.append(validate(model, trainloader,device))\n",
    "\n",
    "        #test_acc_hist.append(validate(model, testloader,device))\n",
    "        # if test_acc_hist[-1][0]>best_acc:\n",
    "        #     best_model=copy.deepcopy(model)\n",
    "        \n",
    "        ep_loss = 0\n",
    "        # 不启用Batch Normalization和Dropout。测试过程中要保证BN层的均值和方差不变，且利用到了所有网络连接，即不进行随机舍弃神经元。\n",
    "        model.eval()\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        with torch.no_grad(): # 停止梯度更新\n",
    "            for images, labels in tqdm(testloader):\n",
    "                images = images.to(device).squeeze()\n",
    "#                 images = torch.sqrt(F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING)))\n",
    "                labels = labels.to(device)\n",
    "                #det_labels = F.one_hot(labels, num_classes=10).to(dtype=torch.float64)\n",
    "                det_labels = labels_image_tensors[labels]\n",
    "\n",
    "                out_label, out_img = model(images)\n",
    "                _, predicted = torch.max(out_label.data, 1)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "                total += labels.size(0)\n",
    "\n",
    "                full_int_img = out_img.sum(axis=(1,2))\n",
    "                loss = loss_function(out_img/full_int_img[:,None,None], det_labels)\n",
    "                #loss = loss_function(out_label, det_labels)\n",
    "                \n",
    "                #直接计算损失，无反向传播和梯度更新。\n",
    "                ep_loss += loss.item()\n",
    "        test_loss_hist.append(ep_loss / len(testloader))\n",
    "        test_acc_hist.append(correct / total)\n",
    "        # 如果最后一次训练的准确率大于之前最好的准确率，则将最后一次的模型保存为最佳模型。\n",
    "        if test_acc_hist[-1]>best_acc:\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            state = {\n",
    "              'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重\n",
    "              'best_acc': best_acc,\n",
    "              'optimizer' : optimizer.state_dict(),\n",
    "            }\n",
    "            torch.save(state, filename)\n",
    "\n",
    "        print(f\"Epoch={epoch} train loss={train_loss_hist[epoch]:.4}, test loss={test_loss_hist[epoch]:.4}\")\n",
    "        print(f\"train acc={train_acc_hist[epoch]:.4}, test acc={test_acc_hist[epoch]:.4}\")\n",
    "        print(\"-----------------------\")\n",
    "        \n",
    "    # 训练完后用最好的一次当做模型最终的结果,等着一会测试\n",
    "    model.load_state_dict(best_model_wts)    \n",
    "    return train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5f71b821",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_loss(imgs, det_labels):\n",
    "    full_int = imgs.sum(dim=(1,2))\n",
    "    loss = 1 - (imgs*det_labels).sum(dim=(1,2))/full_int\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "47e0c342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义衍射模型基本参数\n",
    "wl = 532e-9\n",
    "pixel_size = 10*wl\n",
    "# 定义模型，损失函数和优化器\n",
    "model = DNN(phase = phase, Amp = Amp, num_layers = 5, wl = wl, pixel_size = pixel_size, distance = distance).to(device)\n",
    "# criterion = custom_loss\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=0.002)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "9ce007f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.11it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=9.436, test loss=8.127\n",
      "train acc=0.6715, test acc=0.7394\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.40it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=7.695, test loss=7.24\n",
      "train acc=0.7455, test acc=0.7726\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=7.035, test loss=6.739\n",
      "train acc=0.7741, test acc=0.794\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=3 train loss=6.594, test loss=6.341\n",
      "train acc=0.7964, test acc=0.8187\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=4 train loss=6.229, test loss=5.98\n",
      "train acc=0.816, test acc=0.8385\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:17<00:00, 17.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=5 train loss=5.878, test loss=5.638\n",
      "train acc=0.8341, test acc=0.8468\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:16<00:00, 17.95it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=6 train loss=5.567, test loss=5.354\n",
      "train acc=0.8508, test acc=0.8627\n",
      "-----------------------\n"
     ]
    },
    {
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      "Epoch=7 train loss=5.328, test loss=5.155\n",
      "train acc=0.8613, test acc=0.8711\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=8 train loss=5.155, test loss=5.012\n",
      "train acc=0.8663, test acc=0.8743\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=9 train loss=5.024, test loss=4.897\n",
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      "-----------------------\n"
     ]
    },
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      "Epoch=10 train loss=4.916, test loss=4.797\n",
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      "-----------------------\n"
     ]
    },
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      "Epoch=11 train loss=4.832, test loss=4.723\n",
      "train acc=0.8745, test acc=0.885\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=12 train loss=4.754, test loss=4.641\n",
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      "-----------------------\n"
     ]
    },
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      "Epoch=13 train loss=4.686, test loss=4.572\n",
      "train acc=0.8776, test acc=0.884\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=14 train loss=4.622, test loss=4.52\n",
      "train acc=0.8786, test acc=0.8843\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=15 train loss=4.573, test loss=4.475\n",
      "train acc=0.879, test acc=0.891\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=16 train loss=4.525, test loss=4.433\n",
      "train acc=0.8808, test acc=0.8886\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=17 train loss=4.484, test loss=4.402\n",
      "train acc=0.8813, test acc=0.8895\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=18 train loss=4.452, test loss=4.356\n",
      "train acc=0.8824, test acc=0.8893\n",
      "-----------------------\n"
     ]
    },
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      "Epoch=19 train loss=4.418, test loss=4.319\n",
      "train acc=0.8823, test acc=0.8914\n",
      "-----------------------\n"
     ]
    },
    {
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      "Epoch=20 train loss=4.389, test loss=4.297\n",
      "train acc=0.8835, test acc=0.8882\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Epoch=21 train loss=4.36, test loss=4.277\n",
      "train acc=0.8833, test acc=0.8936\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     "text": [
      "Epoch=22 train loss=4.334, test loss=4.25\n",
      "train acc=0.8838, test acc=0.8925\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     "text": [
      "Epoch=23 train loss=4.31, test loss=4.234\n",
      "train acc=0.8847, test acc=0.8935\n",
      "-----------------------\n"
     ]
    },
    {
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     "text": [
      "Epoch=24 train loss=4.288, test loss=4.206\n",
      "train acc=0.8848, test acc=0.8949\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "Epoch=25 train loss=4.263, test loss=4.181\n",
      "train acc=0.8858, test acc=0.8928\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "Epoch=26 train loss=4.243, test loss=4.162\n",
      "train acc=0.8855, test acc=0.8973\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "Epoch=27 train loss=4.223, test loss=4.143\n",
      "train acc=0.8869, test acc=0.8918\n",
      "-----------------------\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Epoch=28 train loss=4.207, test loss=4.126\n",
      "train acc=0.8864, test acc=0.8957\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch=29 train loss=4.19, test loss=4.102\n",
      "train acc=0.8862, test acc=0.8955\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 正式开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 30,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "1b62795d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[-0.9838,  0.6023,  0.3921,  ...,  0.7540,  0.6066,  0.3951],\n",
      "        [-0.4218,  0.0561,  1.1226,  ...,  1.3031,  1.5843,  1.3930],\n",
      "        [-0.1415,  0.4325,  1.0583,  ...,  0.8824,  0.5710,  0.8612],\n",
      "        ...,\n",
      "        [ 0.4779,  1.3995,  0.8453,  ..., -0.1154,  0.7523,  0.6833],\n",
      "        [ 0.4807,  1.0885,  1.3901,  ...,  0.4145,  1.2445,  1.5847],\n",
      "        [ 0.8612,  1.1382,  1.3988,  ...,  0.3320,  1.1006,  0.4281]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.6373,  1.0703,  0.8684,  ...,  0.4507,  0.9567,  0.0871],\n",
      "        [ 0.1328,  0.7217,  0.2742,  ...,  0.9784,  0.6564,  0.4499],\n",
      "        [ 1.2226,  0.5935,  0.2627,  ...,  0.5265,  1.0180,  0.9034],\n",
      "        ...,\n",
      "        [ 0.2773,  0.6637,  0.3463,  ...,  0.0020,  0.5647,  0.5582],\n",
      "        [ 0.8943,  0.6414, -0.1801,  ...,  0.2233,  1.1795,  0.4972],\n",
      "        [ 0.4872,  1.2133,  0.6041,  ...,  0.1667,  0.5126,  0.0553]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.9854,  0.3629,  0.1713,  ...,  0.8522,  0.3066,  0.5393],\n",
      "        [ 1.1547,  0.3888,  0.6694,  ...,  1.1132,  0.6828,  0.9183],\n",
      "        [ 0.8544,  0.6411,  1.0135,  ...,  0.4484,  1.0014,  0.8491],\n",
      "        ...,\n",
      "        [ 0.5299, -0.2910,  0.0807,  ...,  0.5277,  0.4648,  0.4171],\n",
      "        [ 0.7834, -0.5950,  0.2785,  ..., -0.4705,  0.6507,  0.3531],\n",
      "        [ 0.7590,  0.2823,  0.5914,  ..., -0.1140,  0.2580, -0.7266]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 1.1486,  0.3057,  0.9927,  ...,  0.9232,  0.4854,  0.7245],\n",
      "        [ 0.8150, -0.2506,  0.3751,  ..., -0.2295,  0.2872,  0.5616],\n",
      "        [ 1.0334,  0.2264,  0.3392,  ...,  0.0553,  0.5151,  0.7333],\n",
      "        ...,\n",
      "        [ 1.0731,  0.2107,  0.3836,  ...,  1.1884,  0.0741,  1.3639],\n",
      "        [ 0.0164,  0.4219,  0.1023,  ...,  0.9652,  0.6679,  0.0464],\n",
      "        [ 0.8376, -0.2849,  0.4655,  ...,  0.6479, -0.1909, -0.2678]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.4985,  0.8286,  0.6264,  ...,  0.5234,  0.3290,  0.7552],\n",
      "        [ 0.6860,  1.0876,  0.9861,  ...,  0.3430,  0.6381,  0.2948],\n",
      "        [ 0.1636,  0.4679,  0.8618,  ...,  0.3981, -0.0395,  0.7121],\n",
      "        ...,\n",
      "        [-0.0025,  1.0262,  1.4702,  ...,  0.6831,  0.5224,  0.9614],\n",
      "        [ 0.3580,  0.2428,  0.4066,  ...,  0.3149,  1.0808,  0.3427],\n",
      "        [ 0.6642,  0.7655,  0.9276,  ...,  0.9130,  0.7613,  0.2845]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-2.6422, -2.5631, -2.3801,  ..., -2.4281, -2.4784, -2.1016],\n",
      "        [-2.1562, -2.5225, -2.7466,  ..., -2.6220, -2.7112, -2.5888],\n",
      "        [-1.8601, -1.8006, -2.7449,  ..., -2.3492, -2.8960, -2.5628],\n",
      "        ...,\n",
      "        [-1.7737, -3.0872, -1.0966,  ..., -2.1260, -2.4043, -2.0047],\n",
      "        [-2.5982, -1.9134, -2.3525,  ..., -2.9110, -2.3873, -2.6711],\n",
      "        [-1.8102, -2.3611, -2.6184,  ..., -2.2085, -1.9044, -2.9712]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-1.2043, -0.4303,  0.3490,  ..., -0.4427, -0.0484,  0.7541],\n",
      "        [ 1.2348,  3.9063,  0.8396,  ..., -0.2474, -1.9967,  1.3624],\n",
      "        [-0.0902, -0.8076, -1.3444,  ...,  1.3826,  0.7254, -1.8879],\n",
      "        ...,\n",
      "        [ 0.4374,  0.1312, -1.8247,  ...,  1.6189, -0.2577, -0.3536],\n",
      "        [ 0.7959,  4.5648, -1.4716,  ...,  0.2687, -0.0091,  1.6404],\n",
      "        [ 1.0445, -0.4474,  2.4251,  ..., -1.3497, -0.5432,  2.9658]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-1.9137e-01,  2.4794e+00,  4.6266e-01,  ...,  2.0718e-01,\n",
      "          1.4075e+00,  1.8578e-01],\n",
      "        [-9.6835e-04, -2.4088e-02,  1.2528e+00,  ..., -1.3915e+00,\n",
      "          1.8488e+00, -3.3464e-01],\n",
      "        [ 3.2850e-02, -6.1415e-01, -5.9742e-01,  ..., -4.7117e-01,\n",
      "         -5.2374e-01,  5.0025e+00],\n",
      "        ...,\n",
      "        [ 1.1713e+00,  9.6867e-01,  5.0374e-01,  ...,  3.2966e+00,\n",
      "          4.8507e-02, -1.4803e+00],\n",
      "        [-5.7390e-01, -8.2109e-01,  2.3986e-01,  ..., -1.0393e+00,\n",
      "          6.1686e-01, -6.9045e-01],\n",
      "        [-1.2476e-01,  2.7742e-01, -1.5910e+00,  ..., -1.9013e+00,\n",
      "         -1.2122e+00, -1.9121e+00]], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-0.8470, -0.4858, -3.3019,  ..., -0.9908, -1.4650, -1.4800],\n",
      "        [-1.8993, -2.9076, -2.1212,  ..., -1.9047, -1.7576,  0.4844],\n",
      "        [-2.1455, -2.8058, -0.6083,  ...,  0.2926, -1.3326, -1.6659],\n",
      "        ...,\n",
      "        [-3.0747, -1.4930, -1.1836,  ..., -1.8138, -1.7894, -0.8552],\n",
      "        [-2.2494, -1.4942, -1.3450,  ..., -1.9310, -1.8606, -0.7463],\n",
      "        [-2.2091, -2.0738, -0.7353,  ..., -1.0735, -2.7982, -1.8454]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-0.1111,  0.1451, -1.0069,  ..., -0.7156,  0.1537,  1.0575],\n",
      "        [ 0.0957, -0.3743, -0.7183,  ..., -0.4886,  0.3038,  0.7710],\n",
      "        [-0.9683, -1.4986, -1.8134,  ..., -1.0716, -0.8496, -0.8481],\n",
      "        ...,\n",
      "        [-0.9048, -0.8291, -2.0272,  ..., -1.3680, -0.6489, -0.7034],\n",
      "        [ 0.0667,  0.2052, -1.5693,  ..., -0.6783, -0.4489,  0.0849],\n",
      "        [-0.2796, -0.5169, -0.7085,  ...,  0.0758,  0.9339,  0.4936]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0050], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([0.0030], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([0.0020], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([0.0010], device='cuda:0')\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# 查看训练的各个参数，确保这一次只有相位被训练了（requires_grad=True）\n",
    "for param in model.named_parameters():\n",
    "    print(param[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "5a17d46d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n",
      "\t distance_0\n",
      "\t distance_1\n",
      "\t distance_2\n",
      "\t distance_3\n",
      "\t distance_4\n",
      "\t distance_5\n"
     ]
    }
   ],
   "source": [
    "# 全部解禁，相位和层间距都拿来训练\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = True\n",
    "\n",
    "# for name, param in model.named_parameters():\n",
    "#     if name.find('distance') != -1:\n",
    "#         exec('model.' + name + '.requires_grad_(True)')\n",
    "\n",
    "print(\"Params to learn:\")\n",
    "params_to_update = []\n",
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        params_to_update.append(param)\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ca35e2ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载之前训练好的参数\n",
    "checkpoint = torch.load('best.pt')\n",
    "best_acc = checkpoint['best_acc']\n",
    "model.load_state_dict(checkpoint['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "717642ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载损失函数和优化器\n",
    "criterion = torch.nn.MSELoss(reduction='sum').to(device)\n",
    "# criterion = torch.nn.CrossEntropyLoss().to(device)\n",
    "optimizer = torch.optim.Adam(params_to_update, lr=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "98f7eb5b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.05it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=0 train loss=4.159, test loss=4.086\n",
      "train acc=0.8891, test acc=0.8965\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.34it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=1 train loss=4.149, test loss=4.082\n",
      "train acc=0.8889, test acc=0.8965\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.48it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=2 train loss=4.146, test loss=4.08\n",
      "train acc=0.8884, test acc=0.8963\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:18<00:00, 16.41it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch=3 train loss=4.144, test loss=4.076\n",
      "train acc=0.8885, test acc=0.8961\n",
      "-----------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 重新开启训练\n",
    "train_loss_hist, train_acc_hist, test_loss_hist, test_acc_hist, best_model = train(model, \n",
    "                          criterion,optimizer, train_dataloader, val_dataloader, epochs = 4,  device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "344e84fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[ 0.6431,  0.4833,  0.8031,  ...,  0.5384,  0.7097,  0.2208],\n",
      "        [-0.0837,  0.1288,  1.2072,  ...,  0.4694,  0.4411,  0.9489],\n",
      "        [ 0.8853,  0.5419,  0.6499,  ...,  0.2383,  0.2275,  0.2814],\n",
      "        ...,\n",
      "        [ 0.6345,  0.5225,  0.4692,  ...,  0.8675,  0.3404,  0.0501],\n",
      "        [ 0.3948,  0.1209,  0.7719,  ...,  1.0150,  0.1261,  0.2964],\n",
      "        [ 0.5607,  0.6085,  0.7333,  ...,  0.4668,  1.0453, -0.1000]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.9174,  0.2537,  0.7535,  ...,  0.4043,  0.4600,  0.0922],\n",
      "        [-0.0217,  0.4729,  1.1809,  ...,  0.8328, -0.3390, -0.1193],\n",
      "        [ 0.8926,  1.2378,  1.1023,  ...,  1.0252,  0.0594,  0.3596],\n",
      "        ...,\n",
      "        [ 0.0435,  0.6821,  0.7935,  ...,  0.6290,  0.2357, -0.1543],\n",
      "        [ 0.1809,  0.2682,  0.1268,  ...,  0.7229,  0.1058,  0.0792],\n",
      "        [ 0.9208,  0.6339,  0.2333,  ...,  0.1896,  1.0296,  0.5069]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.3164,  0.5183,  0.2457,  ...,  0.2376,  0.5020,  0.6713],\n",
      "        [-0.2158,  0.5712,  0.1402,  ...,  0.0879,  0.8264,  0.1541],\n",
      "        [ 0.4388,  1.3248,  0.3967,  ...,  0.2752,  0.3514,  0.5308],\n",
      "        ...,\n",
      "        [ 0.4901,  1.1249,  0.8612,  ...,  0.3630,  0.5576, -0.2496],\n",
      "        [ 0.2512,  0.2907,  0.0783,  ...,  0.2522,  0.9674,  0.4417],\n",
      "        [ 0.6307,  0.0742,  0.7116,  ...,  0.6782,  0.4837,  0.4804]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.3478,  0.3672,  0.9258,  ...,  0.4782,  0.3088,  0.9675],\n",
      "        [ 0.7564,  0.5824,  0.2450,  ...,  0.4874,  0.5146,  0.2156],\n",
      "        [ 0.5603,  0.0432,  0.4944,  ...,  0.3371,  0.0835,  1.1271],\n",
      "        ...,\n",
      "        [ 0.2245, -0.0993,  0.6130,  ...,  0.6255,  0.4557,  0.1883],\n",
      "        [ 0.4851, -0.0064,  0.5330,  ...,  0.7285,  1.3432,  0.0586],\n",
      "        [ 0.6834,  0.5148,  0.3176,  ...,  0.4206,  0.8934,  0.8021]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[-0.0734,  0.6901,  0.0853,  ...,  0.9803,  0.5891,  0.3223],\n",
      "        [ 0.0438,  0.8067,  0.1972,  ...,  0.1060,  0.7213,  0.4646],\n",
      "        [ 0.1363, -0.1322,  0.2482,  ...,  1.1555,  0.7594,  0.5206],\n",
      "        ...,\n",
      "        [ 1.0744,  0.8666,  0.2687,  ...,  0.1973,  0.7954,  0.6045],\n",
      "        [ 0.5061,  0.2781,  0.6476,  ...,  0.5418,  0.1829, -0.0687],\n",
      "        [ 0.7670,  0.5251,  0.9132,  ...,  0.7898,  0.4059,  0.1664]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.2397,  0.2040, -0.0680,  ..., -0.2366, -0.2330,  0.1441],\n",
      "        [-0.3205,  0.0467,  0.0292,  ...,  0.0838,  0.0484,  0.0650],\n",
      "        [-0.1057,  0.0463,  0.0490,  ...,  0.1833, -0.0475,  0.3281],\n",
      "        ...,\n",
      "        [-0.2450,  0.1929,  0.0179,  ...,  0.1982, -0.0524,  0.3042],\n",
      "        [ 0.0282,  0.0903,  0.1151,  ...,  0.1017,  0.0114,  0.1769],\n",
      "        [ 0.2788,  0.1910,  0.1969,  ...,  0.0038,  0.2199, -0.0688]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.5133,  0.8272,  0.8771,  ..., -0.0184,  0.4146,  0.0899],\n",
      "        [ 0.3405,  0.8541,  0.9401,  ...,  0.5909, -0.2351,  0.3813],\n",
      "        [ 1.2024,  0.2964,  0.3884,  ...,  1.0197,  0.2720,  0.4517],\n",
      "        ...,\n",
      "        [ 0.3034,  0.5236,  0.5672,  ...,  0.0351,  0.3290,  0.4588],\n",
      "        [ 0.5959,  0.7470,  0.1823,  ...,  0.7871,  0.6696,  0.4727],\n",
      "        [ 0.8611, -0.0591,  0.5697,  ...,  0.5923,  0.7176,  0.7674]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 2.6491,  1.0511,  0.5080,  ...,  1.5479,  0.4574,  1.2015],\n",
      "        [ 1.7072,  0.9216,  1.2585,  ...,  0.7319,  0.4455,  0.6094],\n",
      "        [-0.3048,  0.8667,  1.4490,  ..., -0.7719,  1.9344,  1.3831],\n",
      "        ...,\n",
      "        [ 1.4148,  0.3708,  0.7644,  ...,  0.5743,  0.8847,  0.4587],\n",
      "        [ 1.2651,  1.1852,  0.7301,  ...,  0.3873,  0.9204,  0.9605],\n",
      "        [ 0.9809,  0.6845,  0.3419,  ...,  0.2917,  0.4360,  0.8760]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[ 0.0455,  0.2959,  0.2810,  ...,  0.0420,  0.1021, -0.1204],\n",
      "        [ 0.1201,  0.0698,  0.0501,  ...,  0.0562, -0.1003,  0.0613],\n",
      "        [ 0.1204,  0.1509,  0.1473,  ...,  0.0660,  0.2486,  0.1443],\n",
      "        ...,\n",
      "        [-0.1113,  0.0439,  0.0833,  ..., -0.0080,  0.1239,  0.0414],\n",
      "        [ 0.1201,  0.0136, -0.0670,  ...,  0.0304,  0.0929,  0.0320],\n",
      "        [-0.0280,  0.1078, -0.0874,  ...,  0.0112,  0.0470,  0.0975]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([[2.0971, 2.8210, 2.1239,  ..., 2.1877, 2.6128, 2.9002],\n",
      "        [2.2892, 2.6438, 2.0412,  ..., 1.9927, 2.4301, 2.4863],\n",
      "        [1.6876, 1.9055, 1.4035,  ..., 1.6421, 1.5435, 2.0050],\n",
      "        ...,\n",
      "        [1.2733, 1.8362, 1.4886,  ..., 1.5526, 1.8605, 2.0709],\n",
      "        [2.4173, 2.8737, 2.5427,  ..., 1.9643, 2.4785, 2.3724],\n",
      "        [2.3284, 2.7446, 2.4193,  ..., 2.4151, 2.6104, 2.8567]],\n",
      "       device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0050], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0030], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0020], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0010], device='cuda:0', requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0040], device='cuda:0', requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa9faa1",
   "metadata": {},
   "source": [
    "### ***Saving***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ec77081",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'MNIST_0.7491.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "239983a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 释放显存\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619ac490",
   "metadata": {},
   "source": [
    "### ***Loading***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "771976ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('MNIST_0.7426.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d61eb80",
   "metadata": {},
   "source": [
    "### ***Data Analysis***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "1e175e1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00500000\n",
      "0.00400000\n",
      "0.00300000\n",
      "0.00200000\n",
      "0.00100000\n",
      "0.00400000\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if name.find('distance') != -1:\n",
    "        print('{:.8f}'.format(param.cpu().detach().numpy()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "adac227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t phase_0\n",
      "\t phase_1\n",
      "\t phase_2\n",
      "\t phase_3\n",
      "\t phase_4\n",
      "\t Amp_0\n",
      "\t Amp_1\n",
      "\t Amp_2\n",
      "\t Amp_3\n",
      "\t Amp_4\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if param.requires_grad == True:\n",
    "        print(\"\\t\",name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "ff703d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看\n",
    "def visualize(image, label):\n",
    "    image_E = torch.sqrt(image)\n",
    "    out = model(image_E.to(device))\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    ax1.imshow(image.squeeze(), interpolation='none')\n",
    "    ax1.set_title(f'Input image with\\n total intensity {image.squeeze().sum():.2f}')\n",
    "    output_image = torch.abs(out[1]).detach().cpu()\n",
    "    ax2.imshow(output_image.squeeze(), interpolation='none')\n",
    "    ax2.set_title(f'Output image with\\n total intensity {output_image.squeeze().sum():.2f}')\n",
    "    fig.suptitle(\"label={}\".format(label), x=0.51, y=0.85)\n",
    "    plt.show()\n",
    "\n",
    "def mask_visualiztion():\n",
    "    for name, mask in model.named_parameters():\n",
    "        if name.find('phase') != -1:\n",
    "            plt.imshow(torch.sigmoid(mask.detach().cpu())*360, interpolation = 'none')\n",
    "            plt.title(f'Mask of layer {name}')\n",
    "            plt.colorbar()\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "38979bf8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7Fh4eHo2OjRQEQRBuPJqmobS0FKGhodDrr911YlVVld3XETcGZ2dnm2hkh+FaTC5fvXq1ZjQatffff187fPiw9tRTT2lubm7aqVOnLvvdzMzM+tcBykc+8pGPfBz3k5mZeS2GGE3TNK2yslILDnS6KvUMDg7WKisrr1ldrxXX5Bl4dHQ0+vTpY5Nh3aVLF0yaNAnx8fG/+d3i4mJ4e3vj4e9vh7Ob0eZv63b2ot/p2/uEoo33O0DLLnrrLqoXduWrIbqv+iKEX5I70rI6X/tmG8Z93ZKpvrugjaIdT23FF2Jn6zmf51Nr7r0jUdFWbeZ3RjR/3h5Tqiv/zRIqo254saL1DjpDy54o5i8dyc7ypfro7vxVkdu+7aVopmK+sooH8Haa3blee8hT0TQ797J8e5yjel46nxMe/l0d1Ys6OCtaj9/xtut1vJ2/nA2nenWlUdECNvBo2NI7eShMXS2/yvJZa6Z67nC1nboy3mcnDd5N9XXfRVPd4qa23xLAr9Km9NhF9VU7BlNdX8XvCg4aeETR9qztRst6ZlipXtCVL1trX071gDXqfuj8WA4t6+/Kl3FgayTVOwxWnf9HTgfTst4+6rItFdU4PH0xioqK4OXlRb/XVEpKSuDl5YX05HB4elz5VX5JqRURfU+huLgYnp7qvt2cueq30GtqapCcnIz/+Z//sdHHjBmD7du3K+Wrq6ttXKalpaUAAGc3I5zdbQ8seld+i8Poph7czO78YODkzJehd+UHPbZse/XQmRt3y9/krh44AcBQrR487f2mvQHcycTb70J+U2/n1pFmpz1OJl7eSV1V/7ecKkVzdueFDXV84LDXfnvLYXV0cuYrS+9qp5187IGVrC+rnT3Jya1x7TEY+ADu5Ky2017b7Q3gTmY7ddGpyzEYeVkncy3VrbV29jejvf2NDOAWvgx7+4mTvX7rorZfc+UHeHvLtrd99OB9hW0Le/uJwcgHcL2LnQHcbLGzHHX5Bjv9zejKt5u9fb8xxz0nM++zQOPfnncleHromzSAOzJXvdXnz5+HxWJBUFCQjR4UFFT/GsNfEx8fDy8vr/pPWFjY1a6SIAiCcJNi0axN/jgq1+y05dIzL+2S9zRfZN68eSguLq7/ZGZmXqsqCYIgCDcZVmhN/jgqV/0Wur+/P5ycnJSr7by8POWqHABMJhN9FeOBglDlVrL/Hn6+8Yuz+r7foo78OW2tO7+low+opPqedV0VzWjndjvK+C0m/765VP9sTQzVzX3Pq8tI4m2P+eNOqq9LV+sNAP/5YISidZ10kpY9nMWfeVW24rf0jHaeYVZkqM+VtuR0oWU90niX9B2urhMA2LilN9WDYtS7Pbkpav8DgMd6/Uz1hD9zb0BYnPrsuaiG32/PXh5BdSfefHR7lXs3vjvYQ9F+Ps2XXV1u57FCgZ3bxSHqI478Oyto2RB3/iz1bAF/dlgRzPutZ4pax4oQvl9ty21H9fbD06l+ZJ/6rN98lN9aXl5p55Wqdm4L+ybxdZtoUvc3LZwvw/2WAqrXnuS+CLfd7lQvIqvFsKa1KgI42YPfQjeX8+Nh9gq1b+l68O1T6qwea60V128GkRVWNOUaumnfvrFc9StwZ2dn9O3bFwkJCTZ6QkICBg/mxhBBEARBEBrHNZkHPnfuXDz44IPo168fBg0ahPfeew+nT5/G448/fi1+ThAEQWihWDQNliZMpmrKd28012QAv++++5Cfn4+//e1vyM7ORlRUFNatW4fwcD6NRRAEQRCuhKY+x5Zn4ISZM2di5syZ12rxgiAIgtCikbeRCYIgCA6LFRoscgXevAjzKFTCBHZ0DaFldRbV8Zh6kpc1+fDfc93DXcS/e+hHRVt5dABftjN3nY4KSaX69mHcGZqdoM6F1yaqaWYAsD2PO5ErCnh7dD3VdLGDGaG0rFbBu4dnax655hzB228sV93549vzFLGvPHryuhRyJ66d3BMUbVUd9IZepbTs+7/cQnXXAdy1fWarmrDVbSB38heMVB3eAKDT84PGkWLu/I8MIzMZ5vHOnDOYO67Nt/LZEDmnVPezwZMnl5mNXLee4f2tpCPfQLo6dZ91z+CzGApK3ah+9jR3bXunqd7cPg9yd//RokCqV9bwbV/Wys5vhhcpWs/As7Ts6TK+3Tp25emE+W15+yt/UlMLq2J5H3dO5fuPfkAR1cuTvVXRn/dlLw91Bo/FqXGplE2hJd9Cb5nxNYIgCILg4DTbK3BBEARBuBziQhcEQRAEB8T6f5+mfN9RkVvogiAIguCANNsr8GNLuyhvDqsbyCM8fVqpBq+yCm7kWTt9IdXHbp9F9eWHBipaeCCPQszcxl/Est2DR0GePMtfnek+sFDRytO8admCUv6qvpBUfl5ZNVU1oI0O4ya7DR/w5Dwr9wfC8jVvj9FTNSx9md+PltW7c2OfIYtvzx5D06heQ14PlrqNG/68cnjsY00MN+vpjnuoy87jZihPYvABgE7+eVRPK+DrsKJKbX+beLWfAIDhMzvRmzt5lKxP33y1bDo3Wh3N53289U+8v5UHcWOacZL6mtURQ9VX9wLAJ8ncNBocxvfDc8Xqtkg8wV+b6bmNxx+3vp/HtB4z8dfalh9Q9X3deb+qqOJxrN5ruVktP5qv24G3q68w3b+WZ/TWRfFo3EAP/nrYDF81GndO70207AeLxymapYYb3q4Flia60Jvy3RtNsx3ABUEQBOFyWLQLn6Z831GRAVwQBEFwWOQZuCAIgiAIDoVcgQuCIAgOixU6WHDlry+1NuG7NxoZwAVBEASHxapd+DTl+45Ksx3ALZMKALOt81Znxxlbs12NN6wL4U82Xs8bRXWrlT9N8HBXXcQnDvPoUWcd7wkn97eiumf7IqoXnScuYh/uzr5jxB6qr/9cdc8DgK5O3eSf/cJdvvoB3EFtLeSxmU4defv1JE0zKJw7iAuKuRM3oDePAT2yriPVQ0dlKlpNAI/1dO5aTnVtrzfVAwfnKFruPu7wLmvD1+HeA52obqjkVwMRozMUzWKnz9YQ1z8AtB/OndWM8lLutnYu4b9Z1IEvx3tMNtULytU+tC2vPS1rdOfxrfn7ufN/3KjdvDKEAwF83zy8i89YcOrE+0prvyJFO/9ta1rWxI35KJ/E45L9v1Yd4QCwy7utonmo1QAA1GS68t9cZ+dYdofazh/Pd6ZlK0eoTnZLRRXwb14X4erRbAdwQRAEQbgclibeQm/Kd280MoALgiAIDktLHsDFhS4IgiAIDohcgQuCIAgOi1XTwao1wYXehO/eaGQAFwRBEByWlnwLvdkO4EWFbtBX2eYUG0v5Hf+KLmrurrcPd4vq7eTe9gk/TfUDP6puYX0kzxU2H+JOz8qRvC4lJby8+YSalVzdjbuZU0u5+1nPTevwNKvLGdLnJC3rbeS/+Xkid7i7nLPzRCZaddeWV/M86M6h3G1+MIO7ZYNu4eWzNqmZ3cMmHKRlk76PonqdmfeVJyISFe3FovG0bG0xz3A3deIZ1JajPMe8g4eaHb72p760rJMPr/fhlDZU9whTM987DeOOdT8T78uJR/lsgLGBfDmf7e6vaNVVRlq2dQDPfK/y4nVZn9ZV0fq14fv3mf082P/OkTup/sVP0VR3DVK3j71xocabb5+OfmomPQCkduAu9PbB6m8e68H3k7sHJFH9y9BeVLcWqRnxh07ydzroyIQfa1WzHVpuKmQtC4IgCA6LBXpYmmDn4q/IcgxkABcEQRAcFq2Jz8A1eQYuCIIgCNeflvwMXKaRCYIgCIIDIlfggiAIgsNi0fSwaE14Bi5Z6FcfvZMGvcF2zVoiuCva6Yzq5i4x8iz0H9dy526dC9+KdWFqDrM+T3VoAkApj0+G13ruLB76hwNU37m/p6J1tOPOPro7nOq6AN4eV4saxLx1bW9a1sTjytFhkh1Hby53OZu/U120vWbwtu9f0oPqTl14XXJqeGa3d5Gq7UroRsvWBDTOxrL4ZKyiWXJ4Prx3BKkIADcTz/fO68hv5+3IUTuXZxo/aJXfwh3u+jO8jiV5av/M/IG/d+A4lzF47GGqJ2bzkHSjR7WiWWp5SHjGqQCqm3348SDUT5318EsSz563d/O0qJavK82TT+/Ir1TLe43lOfBn872ofnIdd3n7xOTxuvw/9R0QbrfwQ/raM4Oo3iYmi+rZheo+26+b+n4BANi1leyc1/GutBU6WJtwM9lqZ2aSIyC30AVBEATBAWm2V+CCIAiCcDnExCYIgiAIDsjFZ+BN+TSGJUuWoEePHvD09ISnpycGDRqE77//vv7vmqYhLi4OoaGhcHV1RWxsLA4dOmSzjOrqajz55JPw9/eHm5sbbr/9dpw5c6bRbZcBXBAEQRAaSOvWrfHKK69g9+7d2L17N0aMGIE77rijfpBesGAB3njjDSxevBhJSUkIDg7G6NGjUVpaWr+MOXPmYM2aNVi9ejW2bduGsrIyTJgwARZL4/w4Ok3TmtUT/JKSEnh5eaH1ohehd+VmsUvRVannIc6F/NxkxNi9VD9R4k/19F1qJKepgN9yqejJTTUeu3hkanE3bohx8VWjYW/vkELL7itoTfW8Mm6cKz+iupD89/IuUHQPN0OF+RRRva0Hj4JM/lcvddmdaVHoGxmLZM7i28J463lFO5/HIynbhpEYTAC1xPAHANlHAhXN6s4rHt31BNWTfuHRo/biaPversbAbkvizj6/djx6ND+f9wkXN9VQZ/yJryvfCdz0lL++FdW9x3AjFwveyD7PzV3u7ur+AACWHdxR13ViqqJ5Gfkykj5SDaMAUD6IxyUbUrm5rSpcXYfd2vF1VbSImz1zo/m2twRyw6OzWdVrKnkcrd6Oqdd4lLfHhezK+tvUfQoACrK8Fc1aWYUzc15AcXExPD15X2oqF8eKL/Z3hJsH31cbQnmpBXf1PNakuvr6+uK1117DI488gtDQUMyZMwd/+ctfAFy42g4KCsKrr76KGTNmoLi4GAEBAfjwww9x3333AQDOnj2LsLAwrFu3DrfeemuDf1euwAVBEASHxfp/UapX+rnoYC8pKbH5VFerMyUuxWKxYPXq1SgvL8egQYOQnp6OnJwcjBkzpr6MyWRCTEwMtm/fDgBITk5GbW2tTZnQ0FBERUXVl2koMoALgiAILZ6wsDB4eXnVf+Lj4+2WTUlJgbu7O0wmEx5//HGsWbMGXbt2RU5ODgAgKMj2JVNBQUH1f8vJyYGzszN8fHzslmko4kIXBEEQHJamB7lceISYmZlpcwvdZOJvEgSATp06Yd++fSgqKsIXX3yBadOmITHxv28p1OlsHxFpmqZol9KQMpciV+CCIAiCw2L9v9vgTfkAqHeVX/z81gDu7OyMDh06oF+/foiPj0fPnj3x1ltvITg4GACUK+m8vLz6q/Lg4GDU1NSgsLDQbpmGIgO4IAiC4LBYNF2TP01F0zRUV1cjIiICwcHBSEhIqP9bTU0NEhMTMXjwYABA3759YTQabcpkZ2fj4MGD9WUaSrO9hd6r4ykY3ZxttJSz/GX11gI3RbPnNt+4tRf/wWBuWGBnOJWB3LXtfIy7zQPu4BGENVtUhzsAuLcuUrRTFTwy9FQ+d+LW1fJNq5HI2KIO/DyusojPAjh2nm+HNAt3IgfdrUZBmir4sp0N3M1t+Iq38/xA7uT3JZvIw7eclq1YydtT1prv2M7OquaUw9fh7hLuNteHcFe04RR3BSefVfuK5sbXVXEp74d/H/g11f/flsmKNvnhn2jZXTP7UP32d3j5r9O7U93fXd0WgX4ltGzucT5DpOOtp6iedFSNnXXx4vt3xOQMqvuauAv95yK+Pe/tvVvRPtsxgJZ17s37yh/GbaT6x//iruTSCNVxHrCP99m8GL6fGO1cwoXfq86eOF+pHmcBwMlTdcPrDNw5fzPw3HPPYezYsQgLC0NpaSlWr16NLVu2YP369dDpdJgzZw7mz5+PyMhIREZGYv78+TCbzZgyZQoAwMvLC48++iiefvpp+Pn5wdfXF8888wy6d++OUaNGNaouzXYAFwRBEITLcdFNfuXfb9xM6tzcXDz44IPIzs6Gl5cXevTogfXr12P06NEAgGeffRaVlZWYOXMmCgsLER0djY0bN8LDw6N+GW+++SYMBgPuvfdeVFZWYuTIkVi+fDmcnBo3HU4GcEEQBMFhsWp6WJtgYrM2Mgpl6dKlv/l3nU6HuLg4xMXF2S3j4uKCRYsWYdGiRY367UuRZ+CCIAiC4IDIFbggCILgsFzvW+jNCRnABUEQBIfFCjTJSc5DZh2DZjuApyS3g97F1qmss+PcbdVbzVs+Xc5dyxZPOy7nTO6Kdu9WoGhlh7gjvCaSZ6Efz+Bz+8y9iql+7rRa925+PKHH7Qeeb10wgLtOPU6rZ6o97j5My+4+w13yNTncKe1Uwc+Ci8tVV3SHAJ6rfPBgONU9vPkO+rsBv1B9faaaE16zn/cJgzs/A+82Xs3UBoDdyZHq7016nZb944l7qX7sCM+w/9MTn1N9/r7bFM3jMLHDA3A9x/OwPwwaSHWDh9pXvAzchZ39Z96vvvzPMKqHDOdvWMo4qDr/o/vz9V0QyN3Puj96UF3/P+o+XlXI92+rH+9XRTXcye+Zxg+Z355Vp/8MG6/m1wPA0QJ+PFj1Dnebl3TiQ4y+Rq37yD/9TMueruTHrF/O8Tz94/mq87+mmrddryP7D9OEq06zHcAFQRAE4XL8OozlSr/vqMgALgiCIDgsTY9SddwB3HFrLgiCIAgtGLkCFwRBEBwWK3SwoikmtqZHqd4oGn0FvnXrVkycOBGhoaHQ6XT46quvbP6uaRri4uIQGhoKV1dXxMbG4tChQ1ervoIgCIJQz8Vb6E35OCqNvgIvLy9Hz5498fDDD+Ouu+5S/r5gwQK88cYbWL58OTp27IiXXnoJo0ePRmpqqk2U3OUwFunhZLJdsTUad5KeqlAdkwZzHS1ryuZNtnTgDvKiDG9F8zzLz9j0p3j9XIq4IzP/Th6b5x9WpGghJu5Y3+PK6+J+lDuUnWJUV/2hj7rSstU9+Dp0KbBT7wPc4V/bQ509cPRnNa8aAKKGpFP95Ol2VP8kKZrq+gq1jm58FeKOxxOp/vMT/anu/qy6oBkz5tCyHV7kDv+KDnz7rMgcRHWXJHW2wegpO2nZIyXBVD96kM8qcA5SHecbc3mfqDrmRXWrP3dK53/D3fZmsqsk25n14L2Wu9BT/8z35Xu67VG07z7jL4k4e5TPetDZmVvkeo7/IeZPyepv7uS58V5H+P5Ta+fwqDnz33xr/EpF6+nMZ3fcsukpqusN/NhUnquu89jeR2jZn050UDSr9foNik2fB96CBvCxY8di7Nix9G+apmHhwoV4/vnnMXnyhRckrFixAkFBQVi1ahVmzJjRtNoKgiAIggDgKpvY0tPTkZOTgzFjxtRrJpMJMTEx2L59O/1OdXU1SkpKbD6CIAiC0BCsmq7JH0flqg7gF19ifulLyYOCgpQXnF8kPj4eXl5e9Z+wMH4bTRAEQRAuxfp/t9Cv9OPI88CvSc11OtszGk3TFO0i8+bNQ3Fxcf0nM5O/O1sQBEEQhP9yVaeRBQdfMM/k5OQgJCSkXs/Ly1Ouyi9iMplgMpmuZjUEQRCEFkLTXyfquFfgV3UAj4iIQHBwMBISEtC7d28AQE1NDRITE/Hqq682alk+A3NhcLMd2D1NPAu9g8c5RRvqeYyW/S6iJ9WHeh+n+pJjtyiaqQN3Z3vZqd+9obupfrrGj+prVsQomvv0alq2tC//TZdU7oivOqLmgfsVc5drq7bc0Xo+j5+MGcu4C/08yUI3duJeh6PZgVR/aOomqi/bqq4rAPA9oN7xGfcUd5ufq+H234IuPPPdsF7VM0fSojh1tBPVfXzLqO5hqqH67x9Zq2hvblHz0QEg4Bd+QGrzgPrOAACIDshQtG253PWPMD5bo907vHibf/D9Kr9adTmfLfOkZYMezaN6bTF3xKeWqP1TP6CIlh3Rhh8n1v7IZyBE3MlnSXy7p5eiORfx7WDlExDgxDc9YOT755+S7lM0vRMv6+TM98026/hPur5wVtG2pHSmZQd1VbdxbXkNTvFFX3Us0MHShLncTfnujabRA3hZWRmOH//vBktPT8e+ffvg6+uLNm3aYM6cOZg/fz4iIyMRGRmJ+fPnw2w2Y8qUKVe14oIgCILQkmn0AL57924MHz68/t9z584FAEybNg3Lly/Hs88+i8rKSsycOROFhYWIjo7Gxo0bGzUHXBAEQRAagtxCbwSxsbHQNPuvitPpdIiLi0NcXFxT6iUIgiAIl8WCpt0G5w8XHAPHPfUQBEEQhBZMs32ZSXaOD/SutkYs6wZe3bUD2yja6T78Bfb7j/DoxN3+6jIAAPtUY01lj1JaNC9NjXQFgPh9PE7Sq6MaawoAlUHqHY6EXG4g0edxR0xl21qqGwrVddhh1lFa9kBeCNW7xnJj0tT7f6H6mydHKdqQoJO07I48HrG6u5BvN+cCfg56vp96Xp1TzU1Sh+N7UL0glt9pcg4pVzRfN24mLN8WQHWXmCKqM0MZALy5WTWsebTmRsCi8Uaq6+rs7D+r1ZhRe0aruhBu4Dz+AC8/3uMM1VcvVttzLoZfC5nDeV92/kw1ZALA/sGqua39f3i9v729H9Wt/vw327urhlkASMtor2ihI/mU2PQ9/HgQuJv3N99x/DdzE1spWp9xPLq3qEY1kgLA+Wd4TG36fnV/G9K/4ccJSwU33V4L5Ba6IAiCIDggLfl94DKAC4IgCA6L1sTXiWoOPI3McU89BEEQBKEFI1fggiAIgsMit9AFQRAEwQFp6hvFHPltZM12AHc/7Awnk60VNmc8d/q671djQ4+U8yhIj6giqtcc8KZ6zO17Fa2LG4+kfN9pCNWxm0c+3jn6ANUnRO1XtHt/+T0tGxTFYybPZvKYVudCtbNu38vjPu1NkNx3mru5049FUt3/TtWNW1rHo14zM7iTP6vSiep/vvsbqi/PGKRooaZiWvbv/3yD6q/mDaP6NwnRipZv4m7edqO4E/nE4VCq/+cYd60bytWrhKc7J9Cyf028k+oD2h+h+rYh6rqt+oX3H52VH+yCInjs7lu7eMZs6ANqv3XdFkzLnrLydfLJ396m+gM7HlO0wJcyaNnMzXx2R+8BfJbEmoO9qD7uzmRFO/EYPwYZ7qIyzvXl69aay2fUuJFU26RMPptmQoeDVD9yhkdLLxy3UtH+so9XXLdfDemyVPNjtXB1abYDuCAIgiBcjouvBW3K9x0VGcAFQRAEh6Ul30J33FMPQRAEQWjByBW4IAiC4LBYoYe1CdeiTfnujUYGcEEQBMFhsWg6WJpwG7wp373RNNsB3GICYLLVTCe5c7mij2rHtNbxjaLbz/OTuw5Po/qPaapL9VQId4W6ruPubPf7zlL9k09GUH1ZqxhF0wx23gDnXUbl0ATu2o56RnXVb8/i+eOvdf+C6v/O5u7sA4HcWe1BMrg3Z3DH+r0Dkqj+9beqqxwATldzt3Rurur8X5l6Cy37UdAAqteV8EBwjzy1b5VG8ezssz+EUX3kJHWmAQBs/qk71Z0q1d98/ehoWvbxwVuovr+EZ3CXHVL7s7OdGQh6X55xXbYpiOpD7uTZ3Oy5o3k4X4evt/8P1R879CDVnQxq5Q+e4w53fSe+/5xYymdmeN1RSPUtmR0UrW4yn31SHcbXYbcIfpyomRdI9fRJ6jEhyIu3Z2u2Wj8A0OWaqP6/h+4gy+bvgMhxdlc0q9X+GyuFq0ezHcAFQRAE4XK0ZBObDOCCIAiCw6I18W1kmiSxCYIgCML1xwIdLE14IUlTvnujcdxTD0EQBEFowcgVuCAIguCwWLWmPcd2ZL+dTtO0ZlX9kpISeHl5oc/nf4KTm61D0svE83VPF3BnOaN/61NU35HOc4vbBuUr2mB/npO88pfBVNeb66juv5G76gMeyVC0shruFs08xN21xlLeofVdiZN0L3fPu57jXaPWnS/bWM7LV4xSnbFdgnJo2fOvc0d8Vgy/WeQUQgKhAdzZWXV5f3G4Ny3bJ/w01Xcn28l276D2iZLdPK+7pi3vs8YzfHt2GppO9axV6nopGsqXba3k5+WGAq5rbdR16OtVTstW1/JlWKx8+1ScVR3KAPDamE8UbcHfp9KyVX68v1WE8P7mfVTVijvSorC05utQK+IzEIxFvJ2tt9QoWvqdfF3dM+QXqtvrn7d15E7+tft6qL85/n1a9oGMWKpX1Bmpfniz2vefvOdbWvYf229VNGtlFc7MjkNxcTE8PfnxpalcHCumbb4fzu58ezWEmrIarBi++prW9Voht9AFQRAEwQGRW+iCIAiCw2KFDtYmGNGa8t0bjQzggiAIgsPSkpPY5Ba6IAiCIDggzfYK/L7w3XB1t63eT4XcVGT1Vc+gjp/i0Y7bUvkyRnflRpGt36nGku/6u9Ky7drlUj0jhUeMtnrsONUDXFTTV6mRG94eH5dI9Xk/30V1Xblq9jD3LaZlXc3cIKZ9zdetgRdHVZn6myeM/rRs+TB+Tulsxzx0/0gevfrpZ7GKZolQjUYAUPQXHnf6x/fWU31tjhp3ei6ML9t8lG83QzSP5ExJ5XUxxKgr1+kU74eBKdzcpbNjty3qoJosfV/gpqD0ydzk0yqRx6C2//tBqj+7doqi9Z1xjJYNdeX9c11Cf6o/+udvFC2phJsjkz5TjWAAUNaeZ8nWufF1mHWLakrUXHifOFHG+35Mex7nvPYAj9c15quH74hv/kDL+v/Co5WrybETAKImqduisM6Nlg1vc17R6sqrcYaWvvpYmxjk0pTv3mia7QAuCIIgCJfDiiZGqTrwM3DHPfUQBEEQhBaMXIELgiAIDovWRBe65sBX4DKAC4IgCA6LvI1MEARBEBwQMbE1Q/6dOgROZltnp+WgFy1bHao6YE3ZvGl3376N6tufGUB1yyjVdVpyxI+WrSi3cybnY6Xy/lOtqR7gp8adOjtxV+xbJSP4MhJ5RCKLlKxy4Y5jfVQF1ctieMxmr9ZZvPzaTopW3p23R7PTI/U9uRM5qTCc6r6p6vLPufJ1kj6LR93m1XDHNYvSzSvlkaHlvvw3YeEHjf83jMdVLlw2WdGq+/DtYOnBt1u/wEyq7zmn9sO8v3LXso/hHNVdBvPf7ObO+8QPbl0Vbd+ZVrTsYWceFzxz4vdU/+h0tKJV1/GOZS+ONeJL3icyHub7Mlqr5QPW8T7RawD3Z3+Tyd3mLpl8/6wKUY97bgF8O8Q+yWfZfP/ZIKofzlXX+ZE8PvukKtND0axVPKJWuLo02wFcEARBEC6H3EIXBEEQBAekJUepOu7Nf0EQBEFowcgVuCAIguCwyC10QRAEQXBAWvIArtM0jdswbxAXX9I+66dJMLnbOngrLdyNmbChjypGcoeuczJ3htb0VfPHAUB/RC1fFV5Ny7YK4fnWdVb+pGJgUAbVEzJU17bFjmu5a3AO1c+Wccd+5YZARbPn/A4Yy92yfXy5m/nr9QOprhFDc+g27kI/PZ7XxcmDZ22bDvI88Lpe6vbUTvIs59pAvuzWa7kTO+hPJxRt3y8daNmYoTwLPPEEz+Q3pvH2WI3qbtozhmeHJ+/myw7bYGed3891ho8v308qdvN87+pAvuynh69TtNc3jaNlncrtPOmzc+SytlYd0K5mnkveNZDvPz08uXt+xbrhVK/zVNupc+NO9tbB/DhRs5K7vCv9efsrg9UV8PuJG2nZZZ/dSvXaTvwFBtZa9Tdd3Plxr+4Yd6Gnv/g8iouL4enJZ3M0lYtjxdj1v4fRjY8NDaG2vAbf3/b+Na3rtUKuwAVBEASHpSVfgcsALgiCIDgsLXkAFxe6IAiCIDggcgUuCIIgOCwamjaXu1mZwBqJDOCCIAiCw9KSb6E32wH8hy8GwMnkYqNV+fFzJatZ1U0HudvcYiea2t3Ms3sr69TldGiTR8vmfRNG9YFT91I9o4xnqvusUn/zXC/+tONAHc+PtlRzBzW6qM5Yey7fzF182Sf9uFvWpYbvCCPHJyvauqAoWnZE51Sq7/60B9URwx29d4QfUrTPrb35Mkq4g9WpmudeZxGHf/BO3jfTugdQ3VLKO6KlNXdLj+mhtie/mrvqXduoWfoAcOZB/pte282K9uqc92nZGVunUd3Ymc/60NmZPfHu8omK1ml9AS1b+Q++b546oc6oAIDf99iuaF/+k78z4KSOu46TY9pQvcvADKofOqzu+/rzvF9lVvA+4X53CdU1OwNM3Vn1OOHrZCcf34X3z7BAvs4z83wVrfKc2k8AwL1QrZ+l+voNii15AJdn4IIgCILggDTbK3BBEARBuBxyBS4IgiAIDsjFAbwpn8YQHx+P/v37w8PDA4GBgZg0aRJSU20f/U2fPh06nc7mM3CgbdBVdXU1nnzySfj7+8PNzQ233347zpzh4Vn2kAFcEARBEBpIYmIiZs2ahZ07dyIhIQF1dXUYM2YMystt/Qe33XYbsrOz6z/r1tmmD86ZMwdr1qzB6tWrsW3bNpSVlWHChAmwWBqejNioAbwhZx6apiEuLg6hoaFwdXVFbGwsDh1SDTiCIAiC0FQ0TdfkT2NYv349pk+fjm7duqFnz5744IMPcPr0aSQn25p1TSYTgoOD6z++vv81BhYXF2Pp0qV4/fXXMWrUKPTu3RsfffQRUlJS8MMPPzS4Lo16Bn7xzKN///6oq6vD888/jzFjxuDw4cNwc7vgiF2wYAHeeOMNLF++HB07dsRLL72E0aNHIzU1FR4eamauPSpCrNC72rqAjWV8Rbt0VN2b+nbcdVlx1JvqHTy5A/RMvuoUj/Q8R8saJ/Ezp417ulN9UPc0qhd0YQ5y3h7vzTw72+9+nleelqk6yC0mXm/jKRPVDX48P7nGnWc/HylWf9PJmf/m5uRuVNeH2nHRulVQ/bMjaj6+00m+ruDP63JmCs9I1xep7t9ec47TsikJaq49AJh7FFM93Je76hOOdFE0rY6ffxtc+XZwM/Msa7cJat+ftWsqLet+hDurq3rb+U137iAvD1X71ohVSbTsqhP9qa6v5O0/WBqqaMUjeJ81J3Fndfews1QPcuXHiYqv1Bkbp6fzGQUo5usw1M4xKHstd8QjQu23y/46iS/7sWyqP9ZmG9VfKlJz6S16Plyw2UHWqus3u/pqvQ+8pMR2/ZtMJphM/Bj4a4qLL+zLvx6gAWDLli0IDAyEt7c3YmJi8PLLLyMw8MLMieTkZNTW1mLMmDH15UNDQxEVFYXt27fj1lt5dv2lNOoK/HJnHpqmYeHChXj++ecxefJkREVFYcWKFaioqMCqVasa81OCIAiCcN0ICwuDl5dX/Sc+Pv6y39E0DXPnzsXQoUMRFfXfqbFjx47Fxx9/jE2bNuH1119HUlISRowYgerqCyfROTk5cHZ2ho+Pj83ygoKCkJPDX7DDaJIL/dIzj/T0dOTk5NicVZhMJsTExGD79u2YMWOGsozq6ur6RgHqWZAgCIIg2ONqudAzMzNt3kbWkKvv2bNn48CBA9i2zfZOxn333Vf//1FRUejXrx/Cw8Oxdu1aTJ482e7yNE2DTtfwtlyxiY2deVw8cwgKsr1l+ltnFfHx8TZnPWFhPAxFEARBEC7laj0D9/T0tPlcbgB/8skn8c0332Dz5s1o3br1b5YNCQlBeHg40tIuPDYNDg5GTU0NCgttH5nl5eUp4+dvccUD+MUzj08++UT526VnEL91VjFv3jwUFxfXfzIz+bNbQRAEQbjRaJqG2bNn48svv8SmTZsQERFx2e/k5+cjMzMTISEhAIC+ffvCaDQiISGhvkx2djYOHjyIwYMHN7guV3QL/eKZx9atW23OPIKDgwFcuBK/WFHgt88q7BkFNBcLNBdbk0YIf1c9TvmqpiIXf25aqfPnxqSiKm5wKuylmnOScrmppHqTP9XDbuUGkh0HO1A9dkKKou3MbEvLPnvfN1RflT2Q6q2/Ug1yZ4fx8zirEzeiuBi46auulse3mpzUdehqx1BVdZpvB5fORQ1eNgBoOS6Kpq/jJ5EdIvn2uT+Um6ri105StCwfNV4VAAy8G8LHnZvvThf6UH1gh3RF232a98OoVtyAtfd4ONVLLOr+07PjaVr2lBuvn7WUbzdtGy/vVab2rfdqxpCSQJ0v32d9j/LtucNd3a/cj/EYWdfRPBb5cDY/XhX4cNObW06ZohmN/De99/D95Ewm355PPvY11T96YYKinZ3A11VHA9dfef8+qrvekq9oER1VDQCOHFMNfNZKvl9eC653kMusWbOwatUqfP311/Dw8Ki/u+zl5QVXV1eUlZUhLi4Od911F0JCQpCRkYHnnnsO/v7+uPPOO+vLPvroo3j66afh5+cHX19fPPPMM+jevTtGjRrV4Lo06gr8cmceERERCA4OtjmrqKmpQWJiYqPOKgRBEAShIVzvaWRLlixBcXExYmNjERISUv/59NNPAQBOTk5ISUnBHXfcgY4dO2LatGno2LEjduzYYTMT680338SkSZNw7733YsiQITCbzfj222/h5GTnPRaERl2BX+7MQ6fTYc6cOZg/fz4iIyMRGRmJ+fPnw2w2Y8qUKY35KUEQBEG4LFoTr8AbO4Br2m9PkXN1dcWGDRsuuxwXFxcsWrQIixYtatTv/5pGDeBLliwBAMTGxtroH3zwAaZPnw4AePbZZ1FZWYmZM2eisLAQ0dHR2LhxY6PmgAuCIAiC8Ns0agC/3JkHcMHAFhcXh7i4uCutkyAIgiA0CA1AA4am3/y+oyJvIxMEQRAcFit00F2FJDZHpNkO4M5e1dCbbVfs6Xt5dT32q47jMjN3gPoEllJ9YqsDVH8nY4Sinc/1JCXtu80zz6hxrAAQ3KaA6js2RilanZmfJ/7dMJ7qFeV8DqNzT3UdDh58kJbdmchjTe2dsU7vtpPqxRbVoXwkTXWuAkBA9/NUN67g67DbX1Kp7jlAjfBMW8VjTc9s5dkD753kczuf+p91ipZRxev3VWgg1av3BlM9uDfPS0hb2lnRXL34gSe9NpLqxlZ8yxlL1OWknmlPy+rtpIPq/K1UDxufQfWjp0IUrfOrPMSp/+rDVF+74xaq+wSryyk0qk57APh75Hqqz/vwIaqf9eQu9Ig3sxSt+rAa6QoAM+eo/QcAIk182/s6qQ53ADC/9IWixa+6l5bt3D2X6if78X77cDt1BsaK49G0rCuJVrZU8Ahd4erSbAdwQRAEQbgcV+Ikv/T7jooM4IIgCILDYtV00F3HeeDNCXkfuCAIgiA4IHIFLgiCIDgsmtZEF7oD29BlABcEQRAcFnkG3gzpEHQeRjfbF99nreKh8d2mqy7qn/aqrl0AaNeO5/m+mzCa6jofNUP4+ei1tOyyUzwu1v2oM9XLD/O85bbjTylaNy/ucF+zvT/VW22mMs6rBnecLOYZ7hYXfmpau5fnW3+Qqjr2AeDWEXtU0chdy2WV3D3v8wjPrP45tx3VXY3qdvO+U3UKA8BfIrgTefaaR6j+5dleipZx3M4bhLx4BrVrG+7SvS2EO64/HKu6qH3/40bL5thJLe7Uk+ebZ37fVtFccvi2LxrK660V8z5+aqO6bADQwtWs7HOv8QPpZ8f6UL2W7+LQjqv9c+aYBFISiPsnd5vreLQ9fDrzmSPsOapLHo/E/LmIO/yXrOczSpyLeV2Ke6pTArqNUDPzAeDrvb2o3vZzvs43PttF0Vb3WkrL/v7oA4pWp+PvOhCuLs12ABcEQRCEyyFX4IIgCILggLRkF7oM4IIgCILD0pJNbDKNTBAEQRAcELkCFwRBEByWC1fgTXkGfhUrc51ptgN4foUZTjpbR/LUJ/k7Vpd+fJuiBQw9R8tGuHEXekoozy12clLd0i//cActq6vlncjvHO8h5incWZ52Vs3PPpbC87qNpfw3Y17YRvUQ5yJFS63gudybs7juNpw7wouSeO53rabe6HHz4m7m1t5q/QDgWCZ3eXt4qTnMAFBcruavdwniWdMvHefuX9cO3P6blaT2FQM3HEPnzd249g4aOwv5TAtv9wpFOxurthEA2nXk7ax4jefPVw9VK1Pty+vXJpi7sD3a8HYe9uV9yGuHmilek2pnNkQAX1lhidzh7/O8Oovj32vG0LJ8DQKVbXnoO/f9AydPqv2zy4gMvgwnvuyq9nwd9oo8SXWWj3/Uha/v/p25O33PQ/y48oj/cUV78OWnadmKW9X3S1gqrt/N3ZZsYpNb6IIgCILggDTbK3BBEARBuBwamvZObwe+gy4DuCAIguC4yC10QRAEQRAcCrkCFwRBEByXFnwPvdkO4CXbA+FkcrHRPk/nTlKn+wsVbVrETlr2ra8nUN2tq7oMACivULO5B/Y5RsvuOBBJ9ZJ23KKcf5g7RjW92qPcw7kj2tfMXdgbs+wERRPGtz5E9fK2Fqr7GdQcawCAju8Jx4pVd7qznWUc38NdsX8cy/PKj9lx0K8/1E3Rjibw7VPjxXPZO/dT3cwAcMhXzSXXu3FHNOr4Ta5AH77dhvieoPpXFT3Usr14P0wv4Rbys3fx7aOrVNvvu5/Xu/uos1Sv03gfz/b0pLpltNr+qhojLevztQfVC54op3qYUXVze/HVisIxqrsfAIynuD99SG/uCP/2WD9F09vZH0xOvO9rVn4r199URvWq6apT/HxqW1p2z46OVDeW899cka6+18A4toSWHdVG7Yc1ZbVQa3eNaOItdDjwLfRmO4ALgiAIwuWQJDZBEARBEBwKuQIXBEEQHJaW7EKXAVwQBEFwXDRd055jywB+9fndPZvg4m5rankvYSQtazzgrWhpYTx6k6R6XtB/4MafmY+vU7S3vx1Lyw655QjVdyV2obrVlZvEvIPVaMIxbY7SsnsKuOmr4kceDTv2vh2KtvyXIbSs3otHPuaXqzGYABAyiBuczpepAZSPdtxOy67QBlL9yzO9qd7Gg5sP3Q6r5sMqXzsPu0J5rOvhA22ofvfQXYq276letGzpPHVbAoCbka/bHQXtqJ6T7qdo7p34MsxGbqjrFMGjew161cR2WMfbHunKY3T/+d04qut5FWHuoW43p13c8JY3nC+km53Y3Z/2qQZOHxdSEEBdsTPVPXP5Qf37df2prmun9qH0fH5MOV/BA1mNZt7O1GJ+LBvsrxrqzq/lUbzT4tdQfU0u3686eeQq2qYz3Ai35UwHRbNU8FhY4erSbAdwQRAEQbgcLdnEJgO4IAiC4Li04Hng4kIXBEEQBAdErsAFQRAEh0Vc6IIgCILgqDjwbfCm0GwH8IOlrWC02jpEo6NTadl961WXd0JGJ1p29Ii9japHmUW1r1pbc9fy9mT+m4NjuDvd08iXs35nT0Xb5MQdoMVl3F5b25m7QNd+MUjRuo/h8ZBHz3L3K3MtA8CpLH+q6wxq+SPlIbRsVTJ37lr7cLf5gY1dqQ6e7Ekxmbhr2/kYd9tjqCrld+PbofiY6oYHgFwTX4deR/gu+ehjmxTtP8vUuEsAcKriR7MybizHgFi1f3qk8RW4py9fyNJ7llD97w8/TPUzndXY1NqePF7WbxNft736nKF6xRfqDIycx3lkqu4sd4Rb7R0Z7Tx09PJUY10tG/n+UNCKbx+30/xKME+nRvcCwH/c1W1RPpb35exab6of29mW61ZVbzfwNK/ff9R66Gr4sU24ujTbAVwQBEEQLofcQhcEQRAER6QFu9BlABcEQRAcGN3/fZryfcdEppEJgiAIggMiV+CCIAiC4yK30JsfKes7wclk6z71GMZzmGu8VEev5Rx3l1a34k3ecjyS6tZS1S0b3p7XI8fZg+rHCgOofi7bi+qduqnu2qo6tR4AUKrnLud2YeeonnOstaKl5gTSsrVVfF1VG3mGO3ObA4Cnh+ou/ulrnsHsGp1P9T902Eb1BVkTqH7bgP2KtuGnXrSsbhffDiUdeHu+OqrOEvDiqwQuYTwLvaqSZ3CXD+R52OkVqqM5bFI6LVtWw/tEyTkfqh8vUpcdPJE7jg+c47MHnvxkJtXreMw+vNzUfehcNd9//vjn/1A9pYK/B+DMKHXdGvbydeKbx4/eBd35BnVtVUb1omL1eOPCzeOYPk6dUQAAn5zoS/WBoaeonpyr7svlOTxP/uvTPahe58rb/5/b/6loU5MepWXNYwtUsaIa+DctfvVpwQO43EIXBEEQBAek2V6BC4IgCMJlkdeJCoIgCILj0ZLfRia30AVBEATBAZErcEEQBMFxacEmtuY7gPcuAcy2ed7V33G3tG6omnPsvY3nWKeEcRetwY6z2m2/msN8upZnhJtCeN5y6S/chY5w7jjO/jZc0WqHlNCyVgu/iVJRy13rTz74taIt2MSd3K06cCd7VjrPeO4bxTPV/UxqTvTPfXnW9gPtkqi+106Q99A+PGc+p1J1NAd24e0paMVnLKCG19Gao/aJkhF82we68XzvnCruQndO4f12U2VnRQsKKaJl9Sv59jHepW4HAKioUftKOy8+GyB/NXd+lwzifdnewXGwX7aiJe7j+8kHQdzKXvqpmnkOAB2mqq7tUQFHadnthe2o/pgf71fbivhslf25al08Y4pp2e//Gkv1wFnqOgGAn79RZz0AQK9xah13ZPEZFUUlvF9FRvE8+UcWzlG0rnel0bJHN6rrxFJ9HbPQW/AzcLmFLgiCIAgOSPO9AhcEQRCEy6DTLnya8n1HRQZwQRAEwXGRZ+CCIAiC4IC04GfgjRrAlyxZgiVLliAjIwMA0K1bN7zwwgsYO3YsAEDTNLz44ot47733UFhYiOjoaLz99tvo1q1boytWVeEMvWYbfxg8mRsuqreokYLFXbgpTTvN4yQNxdywVNi/VtF8Ang85i2tTlB9twc3YNV9xE15eQPVuhvtmNWcjnIDVkFXflq5JluNMDUW8WXnFfEsSKcyvq6yyriBJvlohKLpXeto2a+yuGHn7HlvqndulUP1w6dUs6Jbimo+A4DaqGqqm9J5/KbfIPU3DXoeu3r+R260cuM/idJ2vN+2aqXGVWZlcLNazyd4xGr2YdUcCQD6TPUwsLc3PzQE3cONVroaO9GwO3kdt3u3VbTaEG6Es2dW6/gwN6adWthR0f51N6/HI113UJ3tJwBw5gc7+7Kbur/5DC2kZY/E8AHDlKgexwCgxpvvyzv2q+YxJ2++Ds1m3uGy1/H2+B1Vl2M2qMdCADD0U9upq7DTwYWrSqNMbK1bt8Yrr7yC3bt3Y/fu3RgxYgTuuOMOHDp0CACwYMECvPHGG1i8eDGSkpIQHByM0aNHo7SUD3iCIAiC0CS0q/BxUBo1gE+cOBHjxo1Dx44d0bFjR7z88stwd3fHzp07oWkaFi5ciOeffx6TJ09GVFQUVqxYgYqKCqxatepa1V8QBEFoycgA3ngsFgtWr16N8vJyDBo0COnp6cjJycGYMWPqy5hMJsTExGD79u12l1NdXY2SkhKbjyAIgiAIv02jB/CUlBS4u7vDZDLh8ccfx5o1a9C1a1fk5Fx4LhgUZBtyEhQUVP83Rnx8PLy8vOo/YWE8KEIQBEEQFOQKvOF06tQJ+/btw86dO/HEE09g2rRpOHz4cP3fdTpbg4amaYr2a+bNm4fi4uL6T2ZmZmOrJAiCILRULrrQm/JxUBo9jczZ2RkdOnQAAPTr1w9JSUl466238Je//AUAkJOTg5CQ/zqA8/LylKvyX2MymWAyqW7fTq1zYXSzdbYePWtnOV3UuMrQNdwVe3Yidz9b3Ljev50ay7hvq+pyBYCj7mVUzz7H3dnO7fj5U8Au9ZTw3Ei+qQwmfvrYpzV37FtJZ6314g5qvYW7zbVgHpOYm8fb2S4iVy1bokadAkBmlh/VQ0NVFzYAHMkMpnr71mps6nEL7z9dwrmz+syBtlQvLHdVtNpUT1rWGsDXrVcaP2h4pdpx+Luo60Vn4cs4/DOPBzW241GqlWa1b/ULOUvL6u2kXuzboUa9AoBvGm9/bid1/zSncdd/sZ2YWit4+3XkJ9038dka79cNpfr07tyd7v7AQaovf3ecoh06zO8murTi28EpnM9A6OzD3exspoXXFrVvAkBlEI9StXrx7fnAwu8UbcHnd9KyzsXqdtCuZ5RqC6bJUaqapqG6uhoREREIDg5GQkJC/d9qamqQmJiIwYMHN/VnBEEQBEHhYhJbUz6OSqOuwJ977jmMHTsWYWFhKC0txerVq7FlyxasX78eOp0Oc+bMwfz58xEZGYnIyEjMnz8fZrMZU6ZMuVb1FwRBEFoyksTWMHJzc/Hggw8iOzsbXl5e6NGjB9avX4/Ro0cDAJ599llUVlZi5syZ9UEuGzduhIcHv10qCIIgCMKV0agBfOnSpb/5d51Oh7i4OMTFxTWlToIgCIIgXAbJQhcEQRAcFh2a+Dayq1aT60+zHcCPpraC3vWS7Go7G2lMvxRF29qNZxkbT3Pf3rQ7NlF92z3dFa3uOZ7zm/s5z5oOvp3Pg+8YeZzqjLPl3OHdpjN3qO4/z/Ojy7cFKJrfkPO0bOU2nh8dNIo73E8nt6K6qY3q8A/x4oE9pS583dZ8yh3kXndyd/rxk6o7vdO73M2c/QJ3kAeM4+3MW69mVltac7e1MZQ7jqsieHlvN3VGBQCU5qvb31LJHevGUq57evG6FJSoDu09v6g52wDQtV8G1V178e2QHcwfnxkNavurffgObinmM0rYjAoAyOun7uPBffg+GOuvzjIBgFX/GUF1A+9CKB+gbrcAXz4rZXAwz6pP+HwA1Q+F2XknQaXazmK+2RDem/flzF18n/3nvyYrWl0/3jfdPNWVoq+oBv7J63LVacEvM2myC10QBEEQhOtPs70CFwRBEITL0oJd6HIFLgiCIDgu1zlKNT4+Hv3794eHhwcCAwMxadIkpKam2lZJ0xAXF4fQ0FC4uroiNja2/q2dF6mursaTTz4Jf39/uLm54fbbb8eZM/xRhz1kABcEQRCEBpKYmIhZs2Zh586dSEhIQF1dHcaMGYPy8v96TBryau05c+ZgzZo1WL16NbZt24aysjJMmDABFgtP5GPILXRBEATBYWlqmlpjv7t+/Xqbf3/wwQcIDAxEcnIybrnlFuXV2gCwYsUKBAUFYdWqVZgxYwaKi4uxdOlSfPjhhxg1ahQA4KOPPkJYWBh++OEH3HrrrQ2qS7MdwA2eNdCbbW8Q6NN5zu+O1arj/PYH+StMsyq9qf5tVhTVPd9RM32NO1UnNwBUcRnFufw3s4/xL7hmqy5it2zey0pqVEc0AJR05s7KmDv3KtqhBT1oWYs//83sRP6brfbUUj2rk+qgDvMuomXPbuP50bUdeV3qTntTHa7qWWz2LdzJX3mYL/ue8eq6AoBvRhBX9JZAWlZ3mruwvW/l+esTWx2g+nsFw9Rl7OXubMNYPqsgJ8uH6p7+qjvdYztfJyfyec566Ej+EiK38Bqq9/BTs9a/L+b98O4BSVRPLeUzE+68Tc0xX5fRlZb9+mhPqnv2z6d6UaY31Ud2PKZouVV82x8r4X3F8xb1nQEAUGnnHQOoULf/XaN4hvt3n/E467vv3Ub1dcvUjPg2QXymQYCr6ravNdWAp8ZfA67SM/BLX2Vt7z0dl1JcXAwA8PX1BYDLvlp7xowZSE5ORm1trU2Z0NBQREVFYfv27Q0ewOUWuiAIgtDiCQsLs3m1dXx8/GW/o2ka5s6di6FDhyIq6sJFYENerZ2TkwNnZ2f4+PjYLdMQmu0VuCAIgiBclqt0BZ6ZmQlPz/9mQjTk6nv27Nk4cOAAtm1T72Q09tXaDS3za+QKXBAEQXBYrtbbyDw9PW0+lxvAn3zySXzzzTfYvHkzWrf+72PF4OALIVKXXkn/+tXawcHBqKmpQWFhod0yDUEGcEEQBEFoIJqmYfbs2fjyyy+xadMmRERE2Py9Ia/W7tu3L4xGo02Z7OxsHDx4sFGv35Zb6IIgCILjcp2jVGfNmoVVq1bh66+/hoeHR/2VtpeXF1xdXRv0am0vLy88+uijePrpp+Hn5wdfX18888wz6N69e70rvSE02wHcy7MSTmbbvORCkwstq7ulSNE+3zyQlnWq5hvLNY/rXhPVrGSLC3/g0mozd9zm1vB6dxqbRvVDxR0U7Xw/np2tOXPdqYTnYR8pVDPCs8arWeUAEN7KjhM3jd/iOT2O39C5PUxt59lK7qw1DODZ7no7O1moGw+nzjyottNUYMfJH8nnXS5LGE71Dr1Vx3VhX557XXuOz5woPcFnIHywlbtP68JVh3/7+1XnMwCEm7lb+MvcPlR3X6Vui+w7eSZ9wHp+W/F8mZqnDgB/6byB6i/snahoejc+iyHpPH/HQHYhz7BPSVNnSfgkG2lZ/8mqGx4ASj/j7xLQBvE6Hi5U94kwjyJaNnlbJ6rbOwZpHflvRvVXM9XXrB9Ey9a248vIrebr0DKiSNHO7uTrJCNQPX5YK9XZO9eM65zEtmTJEgBAbGysjf7BBx9g+vTpABr2au0333wTBoMB9957LyorKzFy5EgsX74cTk782M1otgO4IAiCIFyO6z0PXNMu/4WGvFrbxcUFixYtwqJFixpXgV8hz8AFQRAEwQGRK3BBEATBcWnBLzORAVwQBEFwXJp4C10G8GuA9+uuMBhszV8lf+SGJes2NSLS2oEbs9zalVK9qh03ueR+oRpojP60KKzPctNXfMRGqj/z+TSqP3THZkULNJaQksCipZOoHrSbm0gMfVSj3cTuPL4z4bv+VO8aq5pnACBtW1uqf31Ajavs2IbHRtYm87hP9OTtz94Vwsub1b1y7NyttOjOnnzbH/ugL9Wr6tTyljNmWja8B49MHRfCgyazqnn7t72rbouyDtxQ9vXmAVQfNCiV6qdaqb9pKHanZau9+FM3/ze4We+5R++kekSoGvfayq2Ilt22uwvV9VW8LsbW7DjBt3HGCW7I9L+D78ujArKovvOsepxoF8Qjbcfdwfe3v/48ierm4zwyNzW7vaKZ1FRcAICxIz92Zj/M9x9jrGpsrBvBjZrMomupuI4mthZMsx3ABUEQBOGyyC10QRAEQXBAWvAALi50QRAEQXBA5ApcEARBcFiu9zzw5oRcgQuCIAiCA9Jsr8AzbneB3sXW3+j3HY8aDPuDGilZXMNdsVmbw6heG8jjNEsGViqalxd3dJ46xeMx5xx9kOr9hvEozGVbYhRNM/DTRKMv153/yt8pe+R4K0XL8PKlZevceUxrej4v79Wbu25LylWfaht3HplamcLjGkv78VkFxV68js6F6rnp1jw1ohYA8r/iDvKwD3ikYX4ntY5GnpYLi5WfI+8tbkP1uwKSqb7NqrrQs4p5HK3ffr6f1ETz9mQfU/utoZzX22LnBU3pt/M/tF/KI1kzxqr9sN+tp2nZd8cuo/q6YnV2AwDsWKiuq5DHTtKy5kVtqd7tWTUuFwAOFXDXdt1+b0X7spbX7397cic76vh2q2jD+76+Wt1G+u58lo22h/eVM/F8P/RzUyNmfexcqrZ1V6N7a8pqcIKWFq4mzXYAFwRBEITL0oJNbDKAC4IgCA5LS34GLgO4IAiC4Ng48CDcFMTEJgiCIAgOiFyBC4IgCI6LPANvfuiDK6G/JM+6kBuUUfhzpKINuOUILVs86BzVK7O8qe7vq+b/BrrxTOCiQjeqh4TxXOUObrwuNT3VzXLiWzX3GACWz1pI9afT7qV6dFfVG5pb4UFKAnlOdhzeBu7YH9v6MNVHehxStD8d4vVzceY3hcwmNcMdAGpC+bZ4KHaXoq09G0XLVpzgDl3zDO6qrzyihuEbyrmDOHtvMNWd+vJ1+1b6SKpXjFXdxbokb1q2tC2VcW9QEtXzu6j91uTEnc9pB/gsDnuc/D3XrbXq9vxy4yBadm3BYKqPv3871fVT8hTtdLE3LTvnpS+o/tKae6huPsu3s2WwGkJuOcOPBy9k8WWHd+MzRwLN3Fm+5xf1uNczWHWPA4B+PHe+n5vLZ0P4vK7+ZmapNy278+seimapvn5Z6C35GbjcQhcEQRAEB6TZXoELgiAIwmWRW+iCIAiC4HjILXRBEARBEBwKuQIXBEEQHBe5hd788PKshJPZ1ql7Ls+Tlg3tlatoyRu70rK6Wv57ukie2XzurLeiFbhxd6m/P3eLWjXuXD1Xw93fJ79RHeeuI7hj/cHkR6heWciz4Ft3K1K0U6ncKa335evEup+7ts+F8vbMzxivLsNORrhlOnd+nzsaSHV9DV+3q5z6KZpxrTctq3Xje3DuaZ75fu+InYr2dVp3Xj872z4jg7fHyczd38Zj6vasCuazAbzbFlH9b8umUr08Qt0pjJ7c9a8L4H3CUsO3p5Oer1vfHc6K5jJZ3Y8B+/tPeR3PX89NU2cJeJzkOfAprbmr3tixhOp1Rbzvm/aox4T7HtxEyy7dPZTq5zbzaTZ1Mdyd7lSlrpe2Zj7j5ZO9A6ium8rXy0Ayqp3NUNcrAHgSw7mOd5NrQwsewOUWuiAIgiA4IM32ClwQBEEQLkdLNrHJAC4IgiA4Li34FroM4IIgCILjIgN48+N8nif0ri62op17HeEe6kvpswMCaNm4kTw6Me6Hu6jO7DNWO3GfHX240azKwldzahE3MvW4S40k3flzF1rWnM0NPkl/eo3q/Tc8pWh9e6rxqoD96M3P26gGMQDY/G0fqqO7au6rzXCnRcvs7EyamUeP1nlw0xeLe83vxZfhls6NPOETTlH98y0D1frZcZPo+E9i8cTlVP++sCfV11aocZVPDU6gZfeXcmPWmdEVVG/rXqBoP+7msbOaiRvnDPlGqjsX8/5Z0FvdbuP9eNzns0E/Un3uqUlUh6e67MqBlbTo50m8L9MdHwAiubnPb4fafqOOryvfAG6QK6jjBrnKVH6cQJjqFPslvy0tGrCVb59zQ7mr19VJ1YPaqP0EAHI11expreT7pXB1abYDuCAIgiBcDnkGLgiCIAiOSAu+hS7TyARBEATBAZErcEEQBMFhkVvogiAIguCItOBb6E0awOPj4/Hcc8/hqaeewsKFCwEAmqbhxRdfxHvvvYfCwkJER0fj7bffRrdu3Rq17IlR+2Fyt3VOrtnZn5atsaou4j49uLPantvcqcxOtGeo6vR0yuYRjkP7p1F9weYJVO/V/STVkzeoMbBOBt7Lagfx+NY7n5pL9cdfVOMd/7VnGC1bR9YrAJwu9qa67xHuus0OMiuaIayclo0I4E7X8lo1ehMAcgt4vG4XPzV+MrmaO3Erq3gE7JEzPGLWqdKeRVnFnjt99g8PUd18iu+SHiSaclHpbQ2uBwD4HuT1Dp+j9luXHL7ta914g7QwkqcJoKoVr4uBRKzu+DefxRDbrRfVXe3UEaFqPwwO4f0qs4Tvy327pFP91LJIqhsr1Pa8+/NwWja21xGqbznH+7JrDl/npiNq3UsmuJCSQEWInT5rJ6Z2y3HSznN8XbkUq/WzVMm14fXgip+BJyUl4b333kOPHrbTWxYsWIA33ngDixcvRlJSEoKDgzF69GiUlvKBRhAEQRCuGO0qfByUKxrAy8rKMHXqVLz//vvw8fGp1zVNw8KFC/H8889j8uTJiIqKwooVK1BRUYFVq1ZdtUoLgiAIAnBhyn5TP47KFQ3gs2bNwvjx4zFq1CgbPT09HTk5ORgzZky9ZjKZEBMTg+3bt9NlVVdXo6SkxOYjCIIgCMJv0+gHFatXr8aePXuQlKSmdOXkXHjuGBQUZKMHBQXh1CmeahUfH48XX3yxsdUQBEEQhBZtYmvUFXhmZiaeeuopfPTRR3Bx4WYJANDpbG9KaJqmaBeZN28eiouL6z+ZmZmNqZIgCILQgrk4jawpH0elUVfgycnJyMvLQ9++fes1i8WCrVu3YvHixUhNTQVw4Uo8JCSkvkxeXp5yVX4Rk8kEk0l1N65L6wa92fYkwVjEzzf2Z7ZWNGsOP8HwSeUnEv0e3Uf1XdnhilZk4ctYd6471f8+6nOur76P6uy0yoPfwID+BM8UH/a/P1P9/Q0jFc25nK/Xwya+zayZblT/6nWevx675hlFqy3g2ydvWxuqV6lxywCATiO4W3hnRoT6m+Xche5Sytsf2S+b6ikVPGuc4XmE/6a1UxnVW3UspnrqMdXO7Z3Cd9/yYXzZvYbwmRnL9g1WtB6jeNmzy9pRvaqzHRd6FW+/aY/abwuiec640cz1bn14dvq+bR0V7f+1/46WnbX3MaqfKPCnekkPfrTXBarTBHy28T6+xaTWDwBQw/vhwDsOUH13jtoPC7N4nroL3wxwzuV9qM5NdfhbXXiwfzWZIWOt5DNSrglyBd4wRo4ciZSUFOzbt6/+069fP0ydOhX79u1Du3btEBwcjISE/75koaamBomJiRg8WD1ICIIgCIJwZTTqCtzDwwNRUbZvKXJzc4Ofn1+9PmfOHMyfPx+RkZGIjIzE/PnzYTabMWXKlKtXa0EQBEG4iANfRTeFqz7b/tlnn0VlZSVmzpxZH+SyceNGeHjwsAxBEARBuFIkSrUJbNmyxebfOp0OcXFxiIuLa+qiBUEQBEGwg+TdCYIgCI5LCzaxNd8BPMcFuGSqmlOVncwcJ9UdqbNT1l4mcI2Vrwr/N1wV7cV/f0LLvnRsPNXfSY+husWF95ygJLU9ldMKaVnfF7i99Mc3h/DfHFKnaM4kyxgAqst5/rh7Hl+Hk+L/THXdYNWh7LVDXa8AUONNZViNfF0dPshd6x6t1UCgAB8e51uxh2eeHz4TQnWnUtWha/FS1ysA1PB4a/i5ctd2ajr/TX2Vuo1q7TyVCrbTzqwK7lCObJWnaFFeZ2nZ8pOhVD+XwyvTqSN3ilu3qX0orQPvb3W5apY+ACTX8m1v9VO3xZwVv6dl9Xb2weIi/ptuWXxfKfNS+4R2G99njYd8qF7nyV3eScRtDgClBepsEO9gvu0rffhxwmyq5XWxqu0cGMqnwpTXqduttrwG12tCcEu+hS7vAxcEQRAEB6T5XoELgiAIwuWQW+iCIAiC4HjILXRBEARBEBwKuQIXBEEQHBe5hd78COhyDgY324z04gruXK4qU7PU/Xuep2ULjvhR3VnPXcQVgarD8rl/T6dlZzy0lupvbB9D9Xm3f031t3MmKVrtLp7NnPdiPtUL83l+dFhogaLNHr6Jll2exZ3sc2ISqP7USu70NR9Qt1vNSJ757eLMXbHWDbz9VRbV/QsAmhqPj/vDkmnZ98x89oCWq/YrAHBpqzp9+4Zyz+12M88OzzrLw93d0rgT+5EH1ivaO+t5v8rM4n3ckGcnEJtMKkhzVbPXAcA0kq9vz6N80akm7vB3mq1mZXcLPUPL6u3c4/QzlVN919fqOwlajzpNy2ac59vBdR9/x0DExJNUH+CToWhfLxxOy9bFqLnpAHBb18NUX/9LT6oHtVP3/ZKfA2nZ6tZ2Zkm48v6mz1f7yrZ9vB6Wzup2sFbwWRbXBBnABUEQBMHxkGfggiAIgiA4FHIFLgiCIDgucgtdEARBEBwPnaZBp135KNyU795omu0A7vF3VxicbE1EVXHc4FSdo+ZV6jfaMWdMqKD6yVJukgp6UjWthDnxl9W/tW4c1Tv24QanBXtupbo1Ql2+1zFuHio65U11uHPTSlWdusmd7JyCHs3kBqS5hffy37TzQKYiVI2IDPycR29aHuCmPNTwOtaqaZIAgLpj3or2VhrfPpZI3q/8dvHdo6y12tCkDVGkJIDISirrDDw208B9WThZGaBood1zadnsvXy71fnzdvoHq7GzNXW8v9Xm8hhQQyXfPj3bcWPagYNtFe1wnZ0Y2Tw7kb6neIcLnaTub6+1/5yWnXRmJtXNA1WzJwBkl/Js3O1W1axYOIpv+9Z+3MC55Zs+VL/t9j1U35YVoWh1UbwD6ap5X3ays24NEWWKZi9aeUoXtX7VZbV4g5YWribNdgAXBEEQhMsit9AFQRAEwfEQF7ogCIIgCA6FXIELgiAIjovcQhcEQRAEx6Ml30JvtgP48Slu0Lu62Gj6VJL5CMBC3LXF7bljsrbQheonzofyiniqy9af48t+cEwi1b9dHEP1p+aso/qiA7GK5v8Ff9pR3J+vE9MpHgN6e98URXt+3x20bPtW56iOOO7YPzWTO2CDfdTo0fPlQbSs6UfVbQ0Ao2fspHrS3/tRPb+r2rVrvPieqqvl69ZnCndQu5P41kxPboef04vH1L7zGY9vLe7OneLf/9yb6gwthEd1OtnptxUn1O1Z3Z3P1uh12zGqp33eker7T5JMWwAgLnxvL/6bRj+1/wBAYShf59Z1YYo2uc8TtOzT/Xks8J6ScKon/acH1avHqtGhljIeXZtZxaNuPfsWUv37ZP6bRm/1N7VTZlo2vO9ZqoMn5iLjlLof/q7vLlp2/aKhimapqQLwHV+4cNVotgO4IAiCIFwWuYUuCIIgCI5HS76FLi50QRAEwXHRrsKnkWzduhUTJ05EaGgodDodvvrqK5u/T58+HTqdzuYzcOBAmzLV1dV48skn4e/vDzc3N9x+++04c4Y/trOHDOCCIAiC0AjKy8vRs2dPLF682G6Z2267DdnZ2fWfdetsPU9z5szBmjVrsHr1amzbtg1lZWWYMGECLBae9MmQW+iCIAiCQ3O9b4OPHTsWY8eO/c0yJpMJwcE81ri4uBhLly7Fhx9+iFGjRgEAPvroI4SFheGHH37ArbfymO1LabYDuK5OB12trcNaZ+GO64AgNVs4v4Q7Pbt3PU31tm48g7ugRnW6btfU3GMAWJ40mOrow8+oli3hTmRPkit9dghvu/8m3nNDH1Ez3AGgsFZ1qTrv5Lnkp6P5b9b9gcrUbQ4AeQdUx7nfEV5v5zK+rr4+2pPqpge5892a4qVoHl14vrXxc1+qp1dxi65rezU7XBfEnd9vf863cXUr7jYPas2dyH0C1FtrP3/Ms7OrQ/my+wxKp/qOo+1VsYg71g/o+DoxxRZR3ZiqbgcAMBarfUtPMvMBoLqWH6YshXymRd0gdfvo6/jNxsQCO+75LVyfMIXPhjhUrOa4ewSqeeIA4GvmGemRXnzWx891auY5ANQdU/fbugD+DoTzG/h2q+7L9x+XTHX7H+sUSMua781R61FeDaykxa8+mnbh05TvAygpse03JpMJJhPvYw1hy5YtCAwMhLe3N2JiYvDyyy8jMPDCOkxOTkZtbS3GjBlTXz40NBRRUVHYvn17gwdwuYUuCIIgtHjCwsLg5eVV/4mPj7/iZY0dOxYff/wxNm3ahNdffx1JSUkYMWIEqqsvnOjn5OTA2dkZPj62LwcKCgpCTo56QmSPZnsFLgiCIAiX42q50DMzM+Hp+d+3zTXl6vu+++6r//+oqCj069cP4eHhWLt2LSZPnmz3e5qmQafjdz4ZcgUuCIIgOC5XyYXu6elp82nKAH4pISEhCA8PR1paGgAgODgYNTU1KCy0fWSWl5eHoCAecsWQAVwQBEEQriH5+fnIzMxESMgFr0Tfvn1hNBqRkPDfJMDs7GwcPHgQgwfb8VIR5Ba6IAiC4LDorBc+Tfl+YykrK8Px48fr/52eno59+/bB19cXvr6+iIuLw1133YWQkBBkZGTgueeeg7+/P+68804AgJeXFx599FE8/fTT8PPzg6+vL5555hl079693pXeEJrtAO4UXAG92XbN1pbyWxqFKWqWs+bGt8rR7dzRmdHFh+ojwtIULShQdb0DQN4RnuP9wMifqL4SQ6jeqVOWop3M46563wT+8CdrOXfKT3x2v6L9PCqPlrUmctfp+zPeofq0jdye7t9Vdfj79ucO3dwyd6q7kfxxAAjzLqL6oUB19oDBzjKKe/N1aAjl2dyVJz0Vzfsof241bc5aqifZydreeaot1X/eoDrOS7pwxzGKeN5/sqZmhAOArlxdL6bzfF257eV6fn9eF1M7O07sT9TZECXRfNkVFXy/N/hxN7dps7p9qgfxsqdL+H5f24bPKliTwjPpO4dnK1p5BnfgB0Rx53dyLs+Nr6rgMwJ0rmq/7duZzzRItvDjgU8iz06vIzHz+07b6T9n1P5mrVJz2q8ZNyBKdffu3Rg+fHj9v+fOnQsAmDZtGpYsWYKUlBSsXLkSRUVFCAkJwfDhw/Hpp5/Cw+O/MwfefPNNGAwG3HvvvaisrMTIkSOxfPlyODnx/YDRbAdwQRAEQWiOxMbGQvuNqWsbNmy47DJcXFywaNEiLFq06IrrIQO4IAiC4LC05Cx0GcAFQRAEx+UqBbk4IjKAC4IgCA6LXIE3Q4J9SmFwq7HRClxcadmKYtUsMjr6AC073kc1cQHAsrPDqJ64bICilURyg5y9OM0NC/iyPe7hZrhb/I8r2ql8HveZ+ghfJ+7p3FT1Y0EXRav4kZvVTLHnqf7Ev2dS3cVIZXhHqgYii5XPYKzZqhoSAaCOe23QdzLfzkXt1PWSlcXXoaGar6tuIaoxCQD2nVcNQQbud8OKN8dR3eLCf/P/zf6M6vP1au5yq8/4Sqny4iYYzcB394I+qgHNdzCP9cw5yvuKPSNQnZ0Y1Ox71H3FeoqbvoYOOEz1n/Z3prqpWN0/dXpewdJKbpDz38T1Dr8/SnVvZ7WPdxzGzaFnKrypfkuAut8DwOrkWKpXhdUo2r6dkbRszwE8Wvl0KK9Law81FtnfhRsSf9bUKF5rxXU0sbVgmu0ALgiCIAiX5Qa40JsLMoALgiAIDktLvoUuSWyCIAiC4IDIFbggCILguIgLXRAEQRAcj5Z8C73ZDuBuzjUwXpIgmJnLYw+tfrWKll3JHa3/rryF6qfW8KjBWmJcDunM3aWeJu68PDKERyR28OCuzpVrRiqaTyp3vuepJvkLdRnB3yl75GPVhV7ag8dgGst5JKclkNfF6sGXc+JQqKK5nW54XCAAWLrwdbVqPd+eliDVoeu3ndvkKwO5I/xsGe9DmqtF0Qq78mUbu5RQvfaIGvcJAH/dNokv57y6/PxuvN7up/kRqWQ4j/D0+lmNry07HEzLho0/y3/Tmc/AOHKGL2dOr82K9u80/hKH1i5FVHf24fub9yNqdG/+7ja0bA34LI6pczdRPa3Cjguf8N2WflT3SOdPLvcN4lGlT93L43jfTFIzs3WteGTs4622UH2PT1uqrz6pRvdmf8HLdrjztKLVlVdDVYWrTbMdwAVBEAThsogLXRAEQRAcj5Z8C11c6IIgCILggMgVuCAIguC4WLULn6Z830Fp1BV4XFwcdDqdzSc4+L8mFU3TEBcXh9DQULi6uiI2NhaHDh266pUWBEEQBAD/fQbelI+D0ugr8G7duuGHH36o//evXz6+YMECvPHGG1i+fDk6duyIl156CaNHj0ZqaqrNi8wbQmpmEPSuti5olzTuio66LVXROrpzp3gPM/dGLjodTvXH4z9Xy54YTkoC01v9TPX/OX0X1dP3tqK6sauaQ1xSxdefKzebI6x/EdWjHldzpVd8z9vjF85dyzklPCfa47Az1cvDVNe6hW9KuGXxvUnbqzqlAUA/qJDqI8LSFO0Hz052ls3d5iNCjlH9k2zV+m/PbV5RyteVLpw7qJ2NqsMdAFz8ybbYwmdlzPrLF1Q/XhVE9VU1/RXNYKce2np1RgEA5Nu5FAgbw13rb+wiDmpuqsfaxKFUN3PjO7KNal954vfradkPlt9GdaOez6hI+qo71a1kUoWJT9awi/U87ytv7ef754DI9AYve02B6ioHgITDXanubFZncQyYepCW3ZakzmyxVl6/LHQdmvgM/KrV5PrT6GfgBoMBwcHB9Z+AgAAAF66+Fy5ciOeffx6TJ09GVFQUVqxYgYqKCqxateqqV1wQBEEQWjKNHsDT0tIQGhqKiIgI3H///Th58sJbbtLT05GTk4MxY8bUlzWZTIiJicH27dvtLq+6uholJSU2H0EQBEFoEBeT2JrycVAaNYBHR0dj5cqV2LBhA95//33k5ORg8ODByM/PR07OhXu5QUG2t+mCgoLq/8aIj4+Hl5dX/ScsjIcZCIIgCMKlXJxG1pSPo9KoAXzs2LG466670L17d4waNQpr115ICFqxYkV9Gd0lD7I0TVO0XzNv3jwUFxfXfzIzMxtTJUEQBEFokTRpHribmxu6d++OtLS0ejf6pVfbeXl5ylX5rzGZTPD09LT5CIIgCEKDEBf6lVFdXY0jR45g2LBhiIiIQHBwMBISEtC7d28AQE1NDRITE/Hqq682etnGTBP0LrauzMpW3BmavDtS0faXd6RlN/fNpXq2Gj8OAHjxy3sVzS+Fb/F5/X5Hdfcsfp4UOv4U1fP+o+Y2v/L0+7TsyrwhVPc28kzkD36MVbTuA0/SskXVPCfaK4XnfvvcnkX16l9U57L7oHO0rNsib76MO3gWutlZzcEHgG+3qjnULnl8O/jG8Ec8B4r5LIFhXVV3+sFzIbSszpP3lZoavutN6MCdvifKAhRtf2c+M+Ht47FU93Sx43x3UddhuB93958dxtd3pB/fngd2tae612l1W/zv7I9o2Re9x1M96CWep582VXWhf7rgVlq2qivfPh8et/OSATsY+qvrKz5qDS07e8sDVH90wDaqL90xjOqHvuqsaGWRfPu8FPMl1bPaelP9xDl/RUv7J3esD5it7g+15TW4XvdSdZoGXROeYzfluzeaRg3gzzzzDCZOnIg2bdogLy8PL730EkpKSjBt2jTodDrMmTMH8+fPR2RkJCIjIzF//nyYzWZMmTLlWtVfEARBEFokjRrAz5w5g9/97nc4f/48AgICMHDgQOzcuRPh4RfmUD/77LOorKzEzJkzUVhYiOjoaGzcuLHRc8AFQRAEoUFY/+/TlO87KI0awFevXv2bf9fpdIiLi0NcXFxT6iQIgiAIDaIl30KXl5kIgiAIggMiLzMRBEEQHBd5H3jzQ1+rg5Pedv544A7uOs2LVZ2Xnh14optVa1zyraW16tzNdeOZ360282XU/YE739POBlJd66o+lJn51aO0rClCzU0HgOoqXkerm5pxnb6GO4Uj7+JZ4Ke6c6ern51EhEnjdyjaN8d5prR5LnczVxXw6YVVlbydd8fuVLS8au7F+OlEB6pXeHC3fVql6ggPX8j7VdfF/GU+3xzsQfVfzrWl+tk09Te7vsJ9vif+wJdRRjL2AcB6UnVtp2W70bIhnfg7Buy5zZ2q+Hop7qb2ofmpPJf8mc4JVH91tDpDBAAeHrlJ0VYEDaRlp0Wp/QQAfshRHd4AcNeDPFXyjUTV5b62VS9aVu/Cc+bXn+UubycPvr8Bav9stYHfVI0/cR/VQ8bwPlSdY1a0uqkFtOyufeosoOuZhd7kNDUHvoXebAdwQRAEQbgcTU1TazFJbIIgCIIgNA/kClwQBEFwXOQWuiAIgiA4HjrrhU9Tvu+oNNsB3LkYcLrk/fY6Kz9TMuaqZo6CEl9a1m8vN9W0nsLjNL1d1EjSc+va0rK5/fmy646rsYQAENmVR4/m7VCjVIv7V9OyleUmqvv48OjRYqjmpPIwbg7Mr+JGJkMB7zaZnt5UP3laNeuZznCDWPBIvk56+HJ9wwY1MhUAvjGoJjnf//D2IJrLrn7FVC8pVQ0+Z57mRqOTm/pT3aWE95WiI8G8fD/VlHnsyXBaFno7VxS1fDu75ql1GfugajwE7JsPYcccWtu6hup399ijaJmVPrRs3Ia7+U9G8mWvfzFG0Zyn8P1hZ0EE1e2ZXd9M5pnLRl/VtLX9wz607HNP8FjTDzO50U7LdaG6YahqKitw4cc9t+jzVM/Y3ZrqQfvVPpRj9KZlPdPUfmWp5n1NuLo02wFcEARBEC6L3EIXBEEQBAekBc8DFxe6IAiCIDggcgUuCIIgOCwtOQtdBnBBEATBcZFn4M2POldAu8Rgfe5W7sR2dqlTNEsVb9q5Qdz97Lo1hOqD7lajEw1P8CjEnMPc0epUyh2ZkZ48NnTgH9IV7cOtQ2lZeKhtB4DKndz5PmDcEUXbWdCRlnU1cGf1Pbf+TPXV2wdR3T+8UNHOazwa1dWJ/+Z3v3BHb2hfHlNrWaE63wNmqesVAEL0fHtW1PGY1lI31flfVqQ60wHA5ziVgQncFcwc7gCgO6bGwPoe5YvW3c/7VWUCj+7VkS60dvVgWrbL+DSq56/kff/0ZCojxFl1+K852pOWnTAkmerJ58OonnWbn6K57vOiZZ1H82jYHDvRvYYzfNZHQJ8iRSvTqxG1APDSTxOpri/jx4lWiXyeU0Gu6jivjlJnzQCAYRM/HvSenEr1AxGhisZbDpR0V/9ireQzBISrS7MdwAVBEAThsmho2ju9HfcCXAZwQRAEwXGRZ+CCIAiC4IhoaOIz8KtWk+uOTCMTBEEQBAdErsAFQRAEx0Vc6M2PdmPSYXSzdQGfLePO0HO5qsN08S0f0bKzf5pKdc3IV8X3p7ooWlsf1VUNAD77uIu0pAPvIMEmnrX98ecjFK1j7Cla9lQ+zz6u9eQOapOTajl2D1NztgHgXDl30X56pi/VNVfu5j6fq263QZ1P0LJ6O/ez9JU8m/psKndWa2NUN3vOYZ4d3rsbd6cXVrlSvaZG7Ss+fqW0bKspfN2mHOUO6tie3Fr+8+koRSuZyPO9dTv4OvG047hu66Vmau/ewWcm7N/dnuohJr7dAjfxfrjIovZxD/9yWvbY452onj2dZ9vrvVUHtEt/vh0OJPH2TB31E9U3eKnHAwAwv6T28ez7+P5gz23uVM37ePYQXr5d39OKprPzcuuqT1RXOQDs3t+B6pGd1XcPpB3kuenOpWq9rVW87dcEKwC+6hr+fQdFbqELgiAIggPSbK/ABUEQBOFyiAtdEARBEByRFvwMXG6hC4IgCIIDIlfggiAIguPSgq/Am+0AXlFnhOGSLGpf1wpa9lyNj6LNTniIlnU/wZtszuMbMS9czabONnCHpd89Z6hu/Yo7jpduu4Xqw8YeVLTcCu7AD/rAheqn7uFZxPvzVDfq7yPVvHcA+KWY51vvPNqZ6k4at4KaI4sU7fQb3OXsOTuT6i7n+c0iUyHfbtGPHVK07St5nvrRM5FUj520h+oJhWr7C87yrO2SI2ouNwCYC+04jiP5dnYiLvy6k3yWQJsY3g/9XLjL++wC1YlsuZVbc72O8P3HezZ38p/YwvuQ7y71nQRl4d607C3LNlG9OEt15gPAiq4rFe2BF56hZe//0zaqf7RpGNV9DvPtdvLxKkXTavl7CrRq7irXnHgfj/iavx/Aa7Cae56c0YaW1U3ly3A6w2daHDsdpGjeqbx+xZ3V46HVeB2t3S14AJdb6IIgCILggDTbK3BBEARBuCwteB64DOCCIAiCwyLTyARBEATBEZFn4IIgCIIgOBI6TWtepx8lJSXw8vLCX7aPhcnd1qm69kw3+p3qWvVGQslZD1q2bWQu1QNcea60lTir92XyTOBgX5637O2iukUB+1nbRYnBilYRwV2kxvP8JoqpC89ZN+jVBz5FuXxd6Wr4+V3HrtzlnJoewpdTpbpuwzvm0LK5ia2obuCrEKVduNte56y208XMy3p/zt3cdQ/mU72gWM3gdkrn29I1hz+cKyHO3d8iKkrNwj+8izu8nSN4LnstyXAHAGuuOpPBauIPBj1D+bJLz3D3/OgBB6j+02k1g7ymWnWmA4CTnVkf5p/4disLUw9pXQZyl/y5Cp6nPiOCZ6G/ueRuqo96aKei/bh8IC1b3Iv3Q6cC3v4xsXupvmO5Oqti2sx1tOw/N99K9aju/B0LKWnqMc5gtuOqJ5q1ogqnHn0JxcXF8PTkfaOpXBwrRrWfA4OT6YqXU2epxg8nFl7Tul4r5Ba6IAiC4LjILXRBEARBEBwJuQIXBEEQHJgmXoHbeYWxIyADuCAIguC4tOBb6M12AE842xlObrbGBH8zj4IMclWNNUec1ShAACj/iL/Y/nwgNxs9Ol01hexNUqMnAaDAmZs8quv4ai6t4MYLU3ShKubZMVp5cLOReZM31Yt6cDMcw1jEn7BYNK53aXeW6kdT1CjZjDP+tOz4O5KpnlPFjXbJqW2p3r7VOUU7nsH7ROAj3FDXxyeb6t+n9VY0YxduYCz2V6N4AcAziJsmqw94U/14QjtFM/TghrK6Or59tCxutHMuVfu+vqcds1oWN/k4F/Df3HioK9V1Feo+4WbHIGdJ9qa6UxU/8Ab2VI2qPbyyaNlVKYOp/qWZx+52uieV6qmlat9yzef7ZpVHNdVH9Uih+k9Z6rYHgPK2avuXLR1Hy6IdNwJml/Lt+bu+uxRtdUo/vmyC1SJPZ68HzXYAFwRBEITLYtXQpNvgVrkCFwRBEITrj2a98GnK9x0Uuc8hCIIgCI1g69atmDhxIkJDQ6HT6fDVV1/Z/F3TNMTFxSE0NBSurq6IjY3FoUO2b0isrq7Gk08+CX9/f7i5ueH222/HmTM8Y8MeMoALgiAIjstFE1tTPo2kvLwcPXv2xOLFi+nfFyxYgDfeeAOLFy9GUlISgoODMXr0aJSW/tfnMWfOHKxZswarV6/Gtm3bUFZWhgkTJsBiaXjIk9xCFwRBEByXG/AMfOzYsRg7diz9m6ZpWLhwIZ5//nlMnjwZALBixQoEBQVh1apVmDFjBoqLi7F06VJ8+OGHGDVqFADgo48+QlhYGH744QfceitPzruUZjuAT2idApdLolT/tWUELZsVpjqAK6t4LGHtLdyFzeI+AWDRxtsUTR9cRct6ufG8z/wi7iA3HOQxjqWB6jOZ6H7HaNkT/+5E9eIx3LGvy1OdyDHRh0hJ4OB5Ho16tog7V6urnKnu5M9dt4zvf1Id3gDgc5jPEvC3c7KaXqRGsvod5cs4n8ZnJqxrzV3rmou6fWoP83Xinc1/s6gP3/bGyAqqm7eqbvbKQ9yZ79HvPNWL9dwRXxWsrsQu/8v3k6P/w3WLD3+OqDunxrQCgI4UrzjF1+Gfp3xN9YX/uYPqBTk+irYqZwAtCw/enswib6rr16rLBoDCKLVBxm5829dW8mPT5i/7Ur2qKz+uuLRTj3tOp7xoWc1ONG7+CV+qf5Idrf6eNz/u+Xmox5q68mpk0tLXgKs0jaykxHZ9mkwmmEyNj2hNT09HTk4OxowZY7OsmJgYbN++HTNmzEBycjJqa2ttyoSGhiIqKgrbt29v8AAut9AFQRCEFk9YWBi8vLzqP/Hx8Ve0nJycC9NSg4JsLwCCgoLq/5aTkwNnZ2f4+PjYLdMQmu0VuCAIgiBcFg1NvAK/8J/MzEybl5lcydX3r9HpbO/AaJqmaEpVGlDm18gVuCAIguC4XCUTm6enp83nSgfw4OALb5O89Eo6Ly+v/qo8ODgYNTU1KCwstFumIcgALgiCIAhXiYiICAQHByMhIaFeq6mpQWJiIgYPvpD817dvXxiNRpsy2dnZOHjwYH2ZhtDoATwrKwsPPPAA/Pz8YDab0atXLyQn/zf+siHz3wRBEAThqmC1Nv3TSMrKyrBv3z7s27cPwAXj2r59+3D69GnodDrMmTMH8+fPx5o1a3Dw4EFMnz4dZrMZU6ZMAQB4eXnh0UcfxdNPP40ff/wRe/fuxQMPPIDu3bvXu9IbQqOegRcWFmLIkCEYPnw4vv/+ewQGBuLEiRPw9vauL3Nx/tvy5cvRsWNHvPTSSxg9ejRSU1Ph4cFds4x1Z7vBcEkWuu8+fr5Re0J1hva6m2cWJx1sT3Wv1sVU7x6o5nv/fIIv43whb19dGXeduufz5zZVQaq+74fOtGz1IO6i9XHjjtHaTNX9fKIzzyU/f463xyWD31qytuO/2StCDSc49BPPk68L5u3J78O3vTmTd2EjyfcuG8Ozts2b+CyBtredpvqogCOK9taukbRsTYCdc2QLf84V6MPrmNVX7UPhbbjbPNDMl2EYqObDA8C5KrX956Jb07I6Hd/Gf+y5merf5XSn+vlP1Xz8goF82y85dgvVLa58/zFkq7MhmOsdALoNO0719E8jqa6/3c4616s/YO94YO+qyWNYHtUrznDne2Wh2s66YTxj39ulhurlh/my/ToWKFpBCZ85Yf6b2s66On7MuybcgJeZ7N69G8OHD6//99y5cwEA06ZNw/Lly/Hss8+isrISM2fORGFhIaKjo7Fx40abMfDNN9+EwWDAvffei8rKSowcORLLly+HkxOfEcVo1AD+6quvIiwsDB988EG91rZt2/r/b8j8N0EQBEFwZGJjY6H9xsCv0+kQFxeHuLg4u2VcXFywaNEiLFq06Irr0ahb6N988w369euHe+65B4GBgejduzfef//9+r9fbv4bo7q6GiUlJTYfQRAEQWgQNyCJrbnQqAH85MmTWLJkCSIjI7FhwwY8/vjj+OMf/4iVK1cCaNj8t0uJj4+3mXsXFqbeWhMEQRAEilVr+sdBadQAbrVa0adPH8yfPx+9e/fGjBkz8Pvf/x5LliyxKdeY+W/z5s1DcXFx/Scz87rl9wiCIAiCw9KoATwkJARdu3a10bp06YLTpy+YfRoy/+1STCaTMv9OEARBEBqCplmb/HFUGmViGzJkCFJTbd3dx44dQ3h4OADb+W+9e1/ItL44/+3VV19tVMU6e+fB2d3WZbmlIz8JqPOqU7SDG3hGeKsh/FZ+bHAa1T/erWYCewdwp6ebiTs9R3bjjvgPXdRlA4DeoHaoOj9+B8PdzHPGiwq4s1rfR83adrNw16PvTp5tXjqctx8kZx0AssvVk7KAvrm0bNV/+DYu6spvc5mKuF48WHVLO1n5OqwYwdtzfGtbqqdXRiiaZ3QhKQmU5HPnbs8O/E7TqSLuCvbwV/Omc3byrPrTfnwd2ptpUZStbh+nbny9hq3mh4zFzjFU9/+c94lSEvu9+JaPaNk5nz9MdXPnIqrX7FXXYeSIk7Rs+mruNm//O/7ugf2Z3J1vMKp58tZafn0UFZFF9YJKnlXfsxOfDXFirTobprKWL6O8lvdDnXroBADcEnxC0b7dzucnT3x/raJVldUh0U78/FVHa+JtcAd+Bt6oAfxPf/oTBg8ejPnz5+Pee+/Frl278N577+G9994DAJv5b5GRkYiMjMT8+fNt5r8JgiAIwlVDa+LbyFrKAN6/f3+sWbMG8+bNw9/+9jdERERg4cKFmDp1an2Zhsx/EwRBEAShaTT6ZSYTJkzAhAkT7P69IfPfBEEQBOGqYLXaT+ppCC3lGbggCIIgNCvkFnrz45e13eFkcrHR6trxqEWXLDW2rypYNZUAgJuRG81WJwzl5c+rxqeq0760bFEod4RsM3A9cjFvz7EnVPPYv4atpGUf//lBqgcm8CjDwtvV33Q18nrcOfsHqu8t4XP1fyng8aj5+wMVrdaf/6bTCB7VqXfi29O4345JLFyNb917PJyW1TnxM/ABI7n58JcUtZ0eW7n5zH8Ej94cF5hC9XXg0aMpmaGKputQScvivJ23KG3g/TZ66lFF232qDS179kG+rvw8VZMdAFT68+1TF6KaL5ecGU5KAp7pVEZNPl/nwWNUk1iUpxqJDAAnvNtRPfkg111y+SHTfFYdBIzjimjZzM/4sg2VfCApn6r2ZQCoDFS3hVMlN2rW+PL9x157TpSp8cqGvtyouWTVeEWzVFcBSKTlhatHsx3ABUEQBOFyaFYrtCbcQm8x08gEQRAEoVnRgm+hy/vABUEQBMEBkStwQRAEwXGxaoCuZV6BywAuCIIgOC6aBqAp08hkAL/qBNxyFgY3WzdtxUrViQsAeSNUR2urtbxpZe15PGjrntlUP3VSdVDrXLijU1fCf/NUrh/VZy7jLs20zaMV7fFtD9Gyrse44zhvsB3X6QESsRrLIzY3n+tI9WMp3IXeMYq7ZXO/Uh3NXYepUY0AUFbH25NylP/mxGc2U33lOtXRbGjDXdvGwzx+cq8Lj80MDs9XNG1bAC1bWO5C9WXpPJaygzd3rbu4qrMnTAn8vQHldl7oZy8etM6qPknr8AafOXFqHP9NSzRft6Vq6iwAwMNLLc/qAQD+9/LY2RN7+fY5la3ub1lJ/NiBHjxG18z2EwDtb+WRrGk/qs5yPxceczxuxo9U9zeWUv3TrH5UD47KU7SsDNU9DgBOpTwu2erMB6+j59Q4XtN6vu1dqtVlWGocd1B0JJrtAC4IgiAIl0OzatCacAtdkytwQRAEQbgBaFY07Ra6TCMTBEEQhOtOS74Cl2lkgiAIguCANLsr8ItnQ3UVqmnHUsNjNq2Vatm6Wt40rZwbS3R6fhvFWqn+pk7jBjFU2lmdpC3AhXfmNvQ3YSeO1UIMJBeWwetoqVbNLHV21omTUyPq9xvLuRCraEttOV8ntXU8CtLeb1aV8UhWa5VaXqvgy7BU8/NYq73yTmo7NXt9094y9Hxd1dqJ+rVUqOXt7g9ctrvOGXUWe+uKm0BZ/S7Uxc5ySPk6sl4BQNPs9Ak7y2bHA2uVnassu32C78v21iHr4/b2B3t9ttLI9zd7y6mrI+8gt7Of6Kq4iU1np0s0pr+BLMNSe6Hs9bi6rdOqm3QbvA58ezgCOq2Z3T84c+YMwsLs2GgFQRAEhyEzMxOtW/PZAk2lqqoKERERyMnJafKygoODkZ6eDhcXPmukudLsBnCr1YqzZ8/Cw8MDpaWlCAsLQ2ZmJjw9+RSGm4GSkhJp501ES2hnS2gjIO28UjRNQ2lpKUJDQ6HXX7sntVVVVaipafidJXs4Ozs73OANNMNb6Hq9vv6MTae7cOvM09Pzpt55LiLtvLloCe1sCW0EpJ1XgpeX11VZzm/h4uLikAPv1UJMbIIgCILggMgALgiCIAgOSLMewE0mE/7617/CZOLxmjcL0s6bi5bQzpbQRkDaKTRvmp2JTRAEQRCEy9Osr8AFQRAEQeDIAC4IgiAIDogM4IIgCILggMgALgiCIAgOiAzggiAIguCANOsB/J133kFERARcXFzQt29f/PTTTze6Sk1i69atmDhxIkJDQ6HT6fDVV1/Z/F3TNMTFxSE0NBSurq6IjY3FoUOHbkxlr5D4+Hj0798fHh4eCAwMxKRJk5CammpT5mZo55IlS9CjR4/65KpBgwbh+++/r//7zdDGS4mPj4dOp8OcOXPqtZuhnXFxcdDpdDaf4ODg+r/fDG28SFZWFh544AH4+fnBbDajV69eSE5Orv/7zdTWlkCzHcA//fRTzJkzB88//zz27t2LYcOGYezYsTh9+vSNrtoVU15ejp49e2Lx4sX07wsWLMAbb7yBxYsXIykpCcHBwRg9ejRKS0uvc02vnMTERMyaNQs7d+5EQkIC6urqMGbMGJSXl9eXuRna2bp1a7zyyivYvXs3du/ejREjRuCOO+6oP9jdDG38NUlJSXjvvffQo0cPG/1maWe3bt2QnZ1d/0lJSan/283SxsLCQgwZMgRGoxHff/89Dh8+jNdffx3e3t71ZW6WtrYYtGbKgAEDtMcff9xG69y5s/Y///M/N6hGVxcA2po1a+r/bbVateDgYO2VV16p16qqqjQvLy/t3XffvQE1vDrk5eVpALTExERN027edmqapvn4+Gj//ve/b7o2lpaWapGRkVpCQoIWExOjPfXUU5qm3Tzb8q9//avWs2dP+rebpY2apml/+ctftKFDh9r9+83U1pZCs7wCr6mpQXJyMsaMGWOjjxkzBtu3b79Btbq2pKenIycnx6bNJpMJMTExDt3m4uJiAICvry+Am7OdFosFq1evRnl5OQYNGnTTtXHWrFkYP348Ro0aZaPfTO1MS0tDaGgoIiIicP/99+PkyZMAbq42fvPNN+jXrx/uueceBAYGonfv3nj//ffr/34ztbWl0CwH8PPnz8NisSAoKMhGDwoKuirvfm2OXGzXzdRmTdMwd+5cDB06FFFRUQBurnampKTA3d0dJpMJjz/+ONasWYOuXbveVG1cvXo19uzZg/j4eOVvN0s7o6OjsXLlSmzYsAHvv/8+cnJyMHjwYOTn5980bQSAkydPYsmSJYiMjMSGDRvw+OOP449//CNWrlwJ4ObZni2JZvc60V9z8XWiF9E0TdFuNm6mNs+ePRsHDhzAtm3blL/dDO3s1KkT9u3bh6KiInzxxReYNm0aEhMT6//u6G3MzMzEU089hY0bN/7mKxsdvZ1jx46t///u3btj0KBBaN++PVasWIGBAwcCcPw2AoDVakW/fv0wf/58AEDv3r1x6NAhLFmyBA899FB9uZuhrS2FZnkF7u/vDycnJ+WsLy8vTzk7vFm46Hq9Wdr85JNP4ptvvsHmzZvr3+8O3FztdHZ2RocOHdCvXz/Ex8ejZ8+eeOutt26aNiYnJyMvLw99+/aFwWCAwWBAYmIi/vnPf8JgMNS3xdHbeSlubm7o3r070tLSbpptCQAhISHo2rWrjdalS5d6Y/DN1NaWQrMcwJ2dndG3b18kJCTY6AkJCRg8ePANqtW1JSIiAsHBwTZtrqmpQWJiokO1WdM0zJ49G19++SU2bdqEiIgIm7/fLO1kaJqG6urqm6aNI0eOREpKCvbt21f/6devH6ZOnYp9+/ahXbt2N0U7L6W6uhpHjhxBSEjITbMtAWDIkCHKlM5jx44hPDwcwM29b9603Cj33OVYvXq1ZjQataVLl2qHDx/W5syZo7m5uWkZGRk3umpXTGlpqbZ3715t7969GgDtjTfe0Pbu3audOnVK0zRNe+WVVzQvLy/tyy+/1FJSUrTf/e53WkhIiFZSUnKDa95wnnjiCc3Ly0vbsmWLlp2dXf+pqKioL3MztHPevHna1q1btfT0dO3AgQPac889p+n1em3jxo2apt0cbWT82oWuaTdHO59++mlty5Yt2smTJ7WdO3dqEyZM0Dw8POqPNTdDGzVN03bt2qUZDAbt5Zdf1tLS0rSPP/5YM5vN2kcffVRf5mZpa0uh2Q7gmqZpb7/9thYeHq45Oztrffr0qZ+K5Khs3rxZA6B8pk2bpmnahWkcf/3rX7Xg4GDNZDJpt9xyi5aSknJjK91IWPsAaB988EF9mZuhnY888kh93wwICNBGjhxZP3hr2s3RRsalA/jN0M777rtPCwkJ0YxGoxYaGqpNnjxZO3ToUP3fb4Y2XuTbb7/VoqKiNJPJpHXu3Fl77733bP5+M7W1JSDvAxcEQRAEB6RZPgMXBEEQBOG3kQFcEARBEBwQGcAFQRAEwQGRAVwQBEEQHBAZwAVBEATBAZEBXBAEQRAcEBnABUEQBMEBkQFcEARBEBwQGcAFQRAEwQGRAVwQBEEQHBAZwAVBEATBAfn/3G99EIyl8kwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_visualiztion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "7343f851",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_ind = np.random.choice(range(len(val_dataset)), size=10, replace=False)\n",
    "for ind in rand_ind:\n",
    "    visualize(val_dataset[ind][0], val_dataset[ind][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "fb04216d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(predicted, labels, conf_matrix):\n",
    "    for p, t in zip(predicted, labels):\n",
    "        conf_matrix[p, t] += 1\n",
    "    return conf_matrix\n",
    "#首先定义一个 分类数*分类数 的空混淆矩阵\n",
    "conf_matrix = torch.zeros(classes_num, classes_num)\n",
    "# 使用torch.no_grad()可以显著降低测试用例的GPU占用\n",
    "with torch.no_grad(): # 停止梯度更新\n",
    "    for i, (images, labels) in enumerate(val_dataloader):\n",
    "        images = images.to(device)\n",
    "        images = torch.sqrt(torch.squeeze(images))\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        out_labels, out_images = model(images)\n",
    "        _, predicted = torch.max(out_labels.data, 1)\n",
    "        \n",
    "        #记录混淆矩阵参数\n",
    "        conf_matrix = confusion_matrix(predicted, labels, conf_matrix)\n",
    "        conf_matrix = conf_matrix.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "d4cd8a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "混淆矩阵总元素个数：10000,测试集总个数:10000\n",
      "[[ 961.    0.   18.   12.    1.   15.   33.    5.   27.   28.]\n",
      " [   0. 1095.    1.    3.    2.    2.    2.   16.    9.    7.]\n",
      " [   0.    3.  909.   27.    8.    7.    3.   26.   16.    6.]\n",
      " [   2.    3.   12.  880.    0.   36.    0.    6.   32.   14.]\n",
      " [   0.    1.   14.    1.  900.    5.    5.   13.   17.   49.]\n",
      " [   3.    1.    3.   21.    0.  763.   16.    2.   20.    8.]\n",
      " [   7.    9.   21.   12.   15.   25.  897.    5.   19.    1.]\n",
      " [   1.    2.   22.   32.    1.   14.    0.  922.   19.   39.]\n",
      " [   6.   21.   31.   16.   12.   18.    2.    2.  804.    8.]\n",
      " [   0.    0.    1.    6.   43.    7.    0.   31.   11.  849.]]\n",
      "每种标签总个数：1100  1137  1005  985  1005  837  1011  1052  920  948\n",
      "每种标签预测正确的个数：961  1095  909  880  900  763  897  922  804  849\n",
      "每种标签的识别准确率为：87.4%  96.3%  90.4%  89.3%  89.6%  91.2%  88.7%  87.6%  87.4%  89.6%"
     ]
    }
   ],
   "source": [
    "conf_matrix = np.array(conf_matrix)# 将混淆矩阵从gpu转到cpu再转到np\n",
    "corrects = conf_matrix.diagonal(offset = 0)#抽取对角线的每种分类的识别正确个数\n",
    "per_classes = conf_matrix.sum(axis = 1)#抽取每个分类数据总的测试条数\n",
    "\n",
    "print(\"混淆矩阵总元素个数：{},测试集总个数:{}\".format(int(np.sum(conf_matrix)), BATCH_SIZE*len(val_dataloader)))\n",
    "np.set_printoptions(suppress=True) \n",
    "print(conf_matrix)\n",
    "\n",
    "# 获取每种label的识别准确率\n",
    "percent = [rate*100 for rate in corrects/per_classes]\n",
    "per_classes = list(per_classes)\n",
    "corrects = list(corrects)\n",
    "\n",
    "print(\"每种标签总个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in per_classes])))\n",
    "print(\"每种标签预测正确的个数：{}\".format('  '.join(['{:.0f}'.format(i) for i in corrects])))\n",
    "print(\"每种标签的识别准确率为：{}\".format('%  '.join(['{:.1f}'.format(i) for i in percent])), end='%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "46a9d760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "label_ticks = list()\n",
    "for i in range(classes_num):\n",
    "    label_ticks = label_ticks + ['{}'.format(classes[i])]\n",
    "\n",
    "# 显示数据\n",
    "plt.imshow(conf_matrix, cmap=plt.cm.Blues)\n",
    "thresh = conf_matrix.max() / 2\t#数值颜色阈值，如果数值超过这个，就颜色加深。\n",
    "for i in range(classes_num):\n",
    "    for j in range(classes_num):\n",
    "        info = int(conf_matrix[j, i])\n",
    "        plt.text(i, j, info,\n",
    "                 verticalalignment='center',\n",
    "                 horizontalalignment='center',\n",
    "                 color=\"white\" if info > thresh else \"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "85bd5d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将模型中的phase导出来\n",
    "phase = []\n",
    "for param in model.named_parameters():\n",
    "    phase.append(param[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "0c82e980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 64])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "2e4d0f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 写一个测试模型，用于查看中间层的输出图案\n",
    "class DNN_test(torch.nn.Module):\n",
    "    def __init__(self, phase=[], Amp = [], num_layers=5, wl = 532e-9, N_pixels = 64, pixel_size = 10*wl, \n",
    "                 distance = []):\n",
    "\n",
    "        super(DNN_test, self).__init__()\n",
    "        \n",
    "        # 定义中间的衍射层\n",
    "        self.diffractive_layers = torch.nn.ModuleList([Diffractive_Layer(wl, N_pixels, pixel_size, distance[i])\n",
    "                                                       for i in range(num_layers)])\n",
    "        # 定义最后的探测层\n",
    "        self.last_diffractive_layer = Propagation_Layer(wl, N_pixels, pixel_size, distance[-1])\n",
    "        self.sofmax = torch.nn.Softmax(dim=-1)\n",
    "    \n",
    "    # 计算多层衍射前向传播\n",
    "    def forward(self, E):\n",
    "        E_out = []\n",
    "        Int = []\n",
    "        for index, layer in enumerate(self.diffractive_layers):\n",
    "            temp = layer(E)\n",
    "            # 这里相当于加了一层sigmoid非线性激活，将相位控制在0到2pi\n",
    "#             constr_phase = 2*torch.pi*torch.sigmoid(self.phase[index])\n",
    "            constr_phase = 2*torch.pi*phase[index]\n",
    "            exp_j_phase = torch.exp(1j*constr_phase) #torch.cos(constr_phase)+1j*torch.sin(constr_phase)\n",
    "            E = temp * exp_j_phase * torch.sigmoid(Amp[index])\n",
    "            E_out.append(E)\n",
    "        E_out.append(self.last_diffractive_layer(E_out[-1]))\n",
    "        for i in range(len(E_out)):\n",
    "            Int.append(torch.abs(E_out[i])**2)\n",
    "        return Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "45221648",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_test = DNN_test(phase = phase, Amp = Amp, num_layers = 5, wl = wl, pixel_size = pixel_size, distance = distance).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "a08c96aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mid_img = model_test(images_E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "66f84499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 64, 64])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_E.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "cead7a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mid_img[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "9b954ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165172822b0>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品的原图\n",
    "plt.imshow(images_E[1].cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "7b6e861a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165178afcd0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U5gw6PEP9JS6h6lLc41YdFyh0fvpNv8sPeOGlKiuXlpzJ9So4r2xUdrm9ni7VVz+b+n/S2hae9InjsZ3JpGN7QcD54v9g/InOfdm/3+EkLpWR+XrDPJfxmEOHHNtdl0rqdxn/hBNG0qtjM4rX7XF/T8gYo2QyqaqqKkUiEbW1taX29fX1qb29XbW1tcf7MAAAH/I0E3rwwQc1Z84cVVZWKpFIaP369dq8ebNeeeUVBQIBNTQ0qLm5WdXV1aqurlZzc7OKi4s1b968bPUfAJDHPIXQv/71L91+++3au3evQqGQLr74Yr3yyiuaPXu2JGnJkiU6ePCgFi1alPqy6qZNm1RaWpqVzgMA8punEHr66aePuj8QCKipqUlNTU3H0ycAwBjB2nEAAGu4qd1hAec8HnQoKBoY73yK0h7nipJ+lyIWM2Ch6uco5fJZO7eXSptsV+qNtkyNx0uFocu17FaN6WRv/5eO7ReOd67g2vPlac4nKnCupnOsHHNbO87rc+jpucrAud2Od6v0HOdSNVfk/Ppk8TfWnZf3ieP83WQmBACwhhACAFhDCAEArCGEAADWEEIAAGuojjvMrZLFofAj+IXLOdwKatyK4GxUfHlZO85NNivY8rUKzk2mxuPlPG7rICacK96Wvfn9tLYN/2ua47Ef/+dkx/a+r5zfSqrjXY7trpVwjsdm8S602b4jrtOp3aoUDzjcbfZoj5nNStd8WjsOAICRIoQAANYQQgAAawghAIA1hBAAwJqAMblVjhSPxxUKhVSn61UYcL4TZBova0V55XRul7W5XCvsvFax5NZLkhv8tqZcDikoKTnuc7jdDdi43InV0Vh/jX00/n5zSJv1omKxmMrKyo56LDMhAIA1hBAAwBpCCABgDSEEALDGH8v2ZPODO6dzG483o8vDDxZzDs9h1gzu32+7C18b66/xGB0/MyEAgDWEEADAGkIIAGANIQQAsIYQAgBY44/qOMAvbCzdUjDO4fGcbzoXGOdwrCQz4HZTSA/99tGyNTh2zIQAANYQQgAAawghAIA1hBAAwBpCCABgDdVxw8nmDfOAI2Xi2vJYZRYoSD/eDLr8+9Tlho6BQud2c6jP+TyOB4+R3yunakTJ/aaYuc7xegtIx/hyMhMCAFhDCAEArCGEAADWEEIAAGsIIQCANflVHWdjbalsrn3FWlmjK1Ovj5dz2OCxL6a//9iPzdcKrlzit+fQ8e7Tx34NMhMCAFhDCAEArCGEAADWEEIAAGvyqzAhlz78deK1f7k+Hr/h9XGWiQIMimwwQsyEAADWEEIAAGsIIQCANYQQAMAaQggAYM1xhVBLS4sCgYAaGhpSbcYYNTU1qaKiQhMmTFBdXZ26urqOt5/2BALpG3KH0+szVl6jTI3dmGPfvJ4DGMaIQ6ijo0OrVq3SxRdfPKR92bJlWr58uVauXKmOjg5FIhHNnj1biUTiuDsLAPCXEYXQl19+qVtvvVWrV6/WySefnGo3xmjFihVaunSp5s6dqylTpmjt2rU6cOCA1q1bl7FOAwD8YUQhdNddd+naa6/VNddcM6S9u7tb0WhU9fX1qbZgMKhZs2Zp69atjudKJpOKx+NDNgDA2OB5xYT169frrbfeUkdHR9q+aDQqSQqHw0Paw+GwPvroI8fztbS06OGHH/baDQCAD3iaCfX09Gjx4sV69tlndcIJJ7geFzjiw1FjTFrbYY2NjYrFYqmtp6fHS5cAAHnM00xo+/bt6u3t1fTp01NtAwMD2rJli1auXKldu3ZJ+npGVF5enjqmt7c3bXZ0WDAYVDAYHEnfRwcVPrltLL8+Y2XsrEvna55mQldffbV27Nihzs7O1DZjxgzdeuut6uzs1Nlnn61IJKK2trbUz/T19am9vV21tbUZ7zwAIL95mgmVlpZqypQpQ9pKSkp06qmnptobGhrU3Nys6upqVVdXq7m5WcXFxZo3b17meg0A8IWM38phyZIlOnjwoBYtWqR9+/appqZGmzZtUmlpaaYfCgCQ5wLG5NYfVuPxuEKhkOp0vQoDRba7A8A2PhPKO/3mkDbrRcViMZWVlR31WNaOAwBYk193VgWQeQXj0tsGB5yPtTErYcbja8yEAADWEEIAAGsIIQCANYQQAMAaQggAYA3VccNxqgaiWgf5yLWybTD90KLxzof2H3I+h1OFneReZecF3xPyNWZCAABrCCEAgDWEEADAGkIIAGANhQnD4cNP+IWHa9m1AMHtHCYDBQiuneF30M+YCQEArCGEAADWEEIAAGsIIQCANYQQAMAaquMApKMiDaOEmRAAwBpCCABgDSEEALCGEAIAWEMIAQCsoToOGOu4cSMsYiYEALCGEAIAWEMIAQCsIYQAANYQQgAAa6iOA8Y6KuFgETMhAIA1hBAAwBpCCABgDSEEALCGwoRMclr+ROKDXxw/ltYZXfwujxpmQgAAawghAIA1hBAAwBpCCABgDSEEALCG6rhMonIG2ZIr19ZYqRrz23hyGDMhAIA1hBAAwBpCCABgDSEEALCGEAIAWOMphJqamhQIBIZskUgktd8Yo6amJlVUVGjChAmqq6tTV1dXxjttXSDgvAF+Z4zzBoyQ55nQ5MmTtXfv3tS2Y8eO1L5ly5Zp+fLlWrlypTo6OhSJRDR79mwlEomMdhoA4A+evydUWFg4ZPZzmDFGK1as0NKlSzV37lxJ0tq1axUOh7Vu3TotWLDA8XzJZFLJZDL1//F43GuXAAB5yvNMaPfu3aqoqFBVVZVuvvlm7dmzR5LU3d2taDSq+vr61LHBYFCzZs3S1q1bXc/X0tKiUCiU2iorK0cwDABAPvIUQjU1NXrmmWf06quvavXq1YpGo6qtrdXnn3+uaDQqSQqHw0N+JhwOp/Y5aWxsVCwWS209PT0jGAYAIB95+nPcnDlzUv89depUzZw5U+ecc47Wrl2rK664QpIUOOIDemNMWts3BYNBBYNBL90AAPjEcZVol5SUaOrUqdq9e3fqc6IjZz29vb1ps6O851YhRNXcseO5yk+8bsiw4wqhZDKp9957T+Xl5aqqqlIkElFbW1tqf19fn9rb21VbW3vcHQUA+I+nP8f94he/0HXXXaczzzxTvb29+tWvfqV4PK758+crEAiooaFBzc3Nqq6uVnV1tZqbm1VcXKx58+Zlq/8AgDzmKYT++c9/6pZbbtFnn32m008/XVdccYW2bdumSZMmSZKWLFmigwcPatGiRdq3b59qamq0adMmlZaWZqXzAID8FjAmt77uHI/HFQqFVKfrVRgost0db8bKvVYygecqP/G64Rj0m0ParBcVi8VUVlZ21GNZOw4AYA13Vh0J/jXozMvzMtafq3zF64YMYyYEALCGEAIAWEMIAQCsIYQAANZQmDASfDjrjOcFgEfMhAAA1hBCAABrCCEAgDWEEADAGkIIAGAN1XHDKCgpSWsLjBvneOxgMunYHhg/3vn4RGLkHUNuKXC+JjQ44Ok0gSLna8X0H3JotFCNONaXrMrA+J3eU0bUlaL0t++BL2IZOfdoYiYEALCGEAIAWEMIAQCsIYQAANYQQgAAa6iOG4bpc6hKci5gcq2aM319GewRfMGlysqxCk7KneqzXOmHLRkYv+v7xIBLJaXLY5qBwePuSy5gJgQAsIYQAgBYQwgBAKwhhAAA1hBCAABrqI4bAdPf7+n4gFslVCY6g5wQKHB7jT2uKee2Bp1xON5v67h5HU++jt+lOk5u7ysu43R7X8k3zIQAANYQQgAAawghAIA1hBAAwBpCCABgDdVxw3C6e6HrGk+DLlU5RS7VMPAN4/baG5f1vVyq4Fyr7JxOk+tVYF55HY/Pxh8IBr39gNu1lQlOlXeeqhQDx1z+y0wIAGANIQQAsIYQAgBYQwgBAKyhMGEYgwcOHPvBbsvzHMrTm9rl67IoNrgtw+PGaRkeZfez5ozgmjhuA/v22e7C8Ly8nk7Hevh5ZkIAAGsIIQCANYQQAMAaQggAYA0hBACwhuq4TPJbhZDfxpNL3G5e51Zl52UZlWzimkCGMRMCAFhDCAEArCGEAADWEEIAAGs8h9Ann3yi2267TaeeeqqKi4t16aWXavv27an9xhg1NTWpoqJCEyZMUF1dnbq6ujLaaQCAP3gKoX379unKK69UUVGRXn75Ze3cuVO/+c1vdNJJJ6WOWbZsmZYvX66VK1eqo6NDkUhEs2fPViKRyHTfgfw1OOC8uTEmfcPYEAg4bz7hqUT78ccfV2VlpdasWZNqO+uss1L/bYzRihUrtHTpUs2dO1eStHbtWoXDYa1bt04LFizITK8BAL7gaSa0ceNGzZgxQzfeeKMmTpyoadOmafXq1an93d3dikajqq+vT7UFg0HNmjVLW7dudTxnMplUPB4fsgEAxgZPIbRnzx61traqurpar776qhYuXKh7771XzzzzjCQpGo1KksLh8JCfC4fDqX1HamlpUSgUSm2VlZUjGQcAIA95CqHBwUFddtllam5u1rRp07RgwQL99Kc/VWtr65DjAkf8vdIYk9Z2WGNjo2KxWGrr6enxOAQAQL7yFELl5eW66KKLhrRdeOGF+vjjjyVJkUhEktJmPb29vWmzo8OCwaDKysqGbACAscFTCF155ZXatWvXkLb3339fkyZNkiRVVVUpEomora0ttb+vr0/t7e2qra3NQHdznM+rWDACXq8JriEcyaky0mt1ZA5fV56q4372s5+ptrZWzc3N+tGPfqQ33nhDq1at0qpVqyR9/We4hoYGNTc3q7q6WtXV1WpublZxcbHmzZuXlQEAAPKXpxC6/PLLtWHDBjU2NuqRRx5RVVWVVqxYoVtvvTV1zJIlS3Tw4EEtWrRI+/btU01NjTZt2qTS0tKMdx4AkN8CxuTWt97i8bhCoZDqdL0KA0W2u+ON2/Q2t55ijCav1wTXELJhlK+rfnNIm/WiYrHYsJ/zs3YcAMAabmo3HC83E8uHf63mys3Rxgqvzy2vBY6UiVlMNq8rx/4FpGN8SGZCAABrCCEAgDWEEADAGkIIAGANIQQAsIbquOH4rVpp1KtksvyYgN/l+u+PU/889JmZEADAGkIIAGANIQQAsIYQAgBYk3OFCYfXU+3XoWNe9gG5gsIEAP99/9b/vJ8fTc6FUCKRkCT9XS9Z7gk8I2sAfEMikVAoFDrqMTl3K4fBwUF9+umnKi0tVSKRUGVlpXp6enx92+94PM44fWQsjHMsjFFinCNljFEikVBFRYUKCo7+qU/OzYQKCgp0xhlnSPr6Tq2SVFZW5usL4DDG6S9jYZxjYYwS4xyJ4WZAh1GYAACwhhACAFiT0yEUDAb10EMPKRgM2u5KVjFOfxkL4xwLY5QY52jIucIEAMDYkdMzIQCAvxFCAABrCCEAgDWEEADAGkIIAGBNTofQU089paqqKp1wwgmaPn26/va3v9nu0nHZsmWLrrvuOlVUVCgQCOiFF14Yst8Yo6amJlVUVGjChAmqq6tTV1eXnc6OUEtLiy6//HKVlpZq4sSJuuGGG7Rr164hx/hhnK2trbr44otT3zCfOXOmXn755dR+P4zxSC0tLQoEAmpoaEi1+WGcTU1NCgQCQ7ZIJJLa74cxHvbJJ5/otttu06mnnqri4mJdeuml2r59e2q/lbGaHLV+/XpTVFRkVq9ebXbu3GkWL15sSkpKzEcffWS7ayP20ksvmaVLl5rnnnvOSDIbNmwYsv+xxx4zpaWl5rnnnjM7duwwN910kykvLzfxeNxOh0fg+9//vlmzZo159913TWdnp7n22mvNmWeeab788svUMX4Y58aNG81f/vIXs2vXLrNr1y7z4IMPmqKiIvPuu+8aY/wxxm964403zFlnnWUuvvhis3jx4lS7H8b50EMPmcmTJ5u9e/emtt7e3tR+P4zRGGP+85//mEmTJpk77rjD/OMf/zDd3d3mr3/9q/nggw9Sx9gYa86G0Le+9S2zcOHCIW0XXHCBeeCBByz1KLOODKHBwUETiUTMY489lmr76quvTCgUMr/73e8s9DAzent7jSTT3t5ujPHvOI0x5uSTTza///3vfTfGRCJhqqurTVtbm5k1a1YqhPwyzoceeshccskljvv8MkZjjLn//vvNVVdd5brf1lhz8s9xfX192r59u+rr64e019fXa+vWrZZ6lV3d3d2KRqNDxhwMBjVr1qy8HnMsFpMknXLKKZL8Oc6BgQGtX79e+/fv18yZM303xrvuukvXXnutrrnmmiHtfhrn7t27VVFRoaqqKt18883as2ePJH+NcePGjZoxY4ZuvPFGTZw4UdOmTdPq1atT+22NNSdD6LPPPtPAwIDC4fCQ9nA4rGg0aqlX2XV4XH4aszFG9913n6666ipNmTJFkr/GuWPHDp144okKBoNauHChNmzYoIsuushXY1y/fr3eeusttbS0pO3zyzhramr0zDPP6NVXX9Xq1asVjUZVW1urzz//3DdjlKQ9e/aotbVV1dXVevXVV7Vw4ULde++9euaZZyTZez1z7lYO33T4Vg6HGWPS2vzGT2O+++679c477+jvf/972j4/jPP8889XZ2envvjiCz333HOaP3++2tvbU/vzfYw9PT1avHixNm3apBNOOMH1uHwf55w5c1L/PXXqVM2cOVPnnHOO1q5dqyuuuEJS/o9R+vpebTNmzFBzc7Mkadq0aerq6lJra6t+/OMfp44b7bHm5EzotNNO07hx49LSt7e3Ny2l/eJwNY5fxnzPPfdo48aNev3111P3h5L8Nc7x48fr3HPP1YwZM9TS0qJLLrlETzzxhG/GuH37dvX29mr69OkqLCxUYWGh2tvb9dvf/laFhYWpseT7OI9UUlKiqVOnavfu3b55LSWpvLxcF1100ZC2Cy+8UB9//LEke7+bORlC48eP1/Tp09XW1jakva2tTbW1tZZ6lV1VVVWKRCJDxtzX16f29va8GrMxRnfffbeef/55vfbaa6qqqhqy3y/jdGKMUTKZ9M0Yr776au3YsUOdnZ2pbcaMGbr11lvV2dmps88+2xfjPFIymdR7772n8vJy37yWknTllVemfV3i/fff16RJkyRZ/N3MWsnDcTpcov3000+bnTt3moaGBlNSUmI+/PBD210bsUQiYd5++23z9ttvG0lm+fLl5u23306VnT/22GMmFAqZ559/3uzYscPccssteVcKeuedd5pQKGQ2b948pOT1wIEDqWP8MM7GxkazZcsW093dbd555x3z4IMPmoKCArNp0yZjjD/G6OSb1XHG+GOcP//5z83mzZvNnj17zLZt28wPf/hDU1pamnqv8cMYjfm6zL6wsNA8+uijZvfu3eaPf/yjKS4uNs8++2zqGBtjzdkQMsaYJ5980kyaNMmMHz/eXHbZZaky33z1+uuvG0lp2/z5840xX5dIPvTQQyYSiZhgMGi+853vmB07dtjttEdO45Nk1qxZkzrGD+P8yU9+kro2Tz/9dHP11VenAsgYf4zRyZEh5IdxHv4uTFFRkamoqDBz5841XV1dqf1+GONhf/7zn82UKVNMMBg0F1xwgVm1atWQ/TbGyv2EAADW5ORnQgCAsYEQAgBYQwgBAKwhhAAA1hBCAABrCCEAgDWEEADAGkIIAGANIQQAsIYQAgBYQwgBAKz5/4TFvmMvaS/KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch里第2个样品在第1层的图案\n",
    "plt.imshow(mid_img[5][1].cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b551ae2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "D2NN",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
